Test masks for lithographic and etch processes

ABSTRACT

A mask design is generated for patterning a test wafer using a lithographic or etch process, the process is characterized based on the patterned test wafer, and a pattern-dependent model is used based on the characterization to predict characteristics of integrated circuits that are to be fabricated by the lithographic or etch process.

[0001] This application is a continuation in part of, and claims thebenefit of priority of, U.S. patent application Ser. Nos. 10/165,214,10/164,844, 10/164,847, and 10/164,842, all filed Jun. 7, 2002, and Ser.No. 10/200,660, filed Jul. 22, 2002, all assigned to the same assigneeas this patent application. The contents of those patent applicationsare incorporated by reference here.

BACKGROUND

[0002] This description relates to lithography mask creation forintegrated circuits (ICs).

[0003] Lithography mask creation and printing assume that projection isdone on a film, within a predetermined depth of focus range. Howeverpattern dependencies between the process by which the ICs are fabricatedand the pattern that is being created often cause processed films tohave significant variation in thickness across a surface, resulting invariation in feature dimensions (e.g. line widths) of integratedcircuits (ICs) that are patterned using the mask. As successivenon-conformal layers are deposited and polished, the variation becomesworse. Because interconnect lines and connections on higher layers carrypower to portions of the chip, the variations can increase the sheetresistance and thus affect the power effectiveness of the chip.

[0004] One way to reduce the variations in fabricated chips is to makephysical measurements on manufactured wafers containing initial designsof devices and use these measurements to adjust the mask design. Othermethods to reduce variation include optical proximity correction (OPC)where subwavelength distortions due to patterned features are identifiedand corrected.

SUMMARY

[0005] In general, in one aspect, the invention features a method thatincludes generating a mask design for patterning a test wafer using alithographic or etch process, characterizing the process based on thepatterned test wafer, and using a pattern-dependent model based on thecharacterization to predict characteristics of integrated circuits thatare to be fabricated by the lithographic or etch process.

[0006] Implementations of the invention may include one or more of thefollowing features. The mask design comprises various line and spacedimensions. The mask design includes multiple masks for multiple levels,each of the masks including various line and space dimensions. Theprocess comprises a preselected lithography or etch tool or lithographyor etch recipe. The process comprises preselected power settings. Theprocess comprises preselected etch times. The process comprisespreselected polish times. The process comprises preselected depositiontimes. The process comprises preselected pressures. An electronicsdesign automation (EDA) tool is used in conjunction with at least one ofthe generating, the characterizing, and the predicting. At least one ofthe generating, the characterizing, and the predicting is provided as aservice in a network. The network comprises an intranet, an extranet, oran internet, and the generating, the characterizing, and the predictingis provided in response to user requests. The recipe includes apreselected photoresist.

[0007] Other advantages and features of the invention will becomeapparent from the following description and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[0008]FIG. 1 illustrates how lithography works.

[0009]FIG. 2 describes the process for using IC designs and patterns tocreate lithography masks.

[0010]FIG. 3 illustrates a case in which the focal distance to analignment key is proper; but chip-level variation is outside the depthof focus limits.

[0011]FIG. 4 shows where lithography fits within a damascene process.

[0012]FIG. 5 illustrates pattern dependencies for electroplated copperdeposition (ECD).

[0013]FIG. 6A illustrates film thickness variation that results fromoxide chemical mechanical polishing (CMP).

[0014]FIG. 6B illustrates erosion, dishing and corner rounding effectsassociated with a CMP step used in a process of forming of shallowtrench isolation (STI).

[0015]FIG. 6C illustrates copper dishing, dielectric erosion andresidual copper effects associated with a copper CMP step used indamascene processes.

[0016]FIG. 7A illustrates a top-down view of different density featureswithin a squareregion.

[0017]FIG. 7B illustrates the variation in oxide thickness for featureswithin a region.

[0018]FIG. 8 illustrates how surface topography may affect printedfeature dimensions.

[0019]FIG. 9 illustrates how feature density may affect printed featuredimensions.

[0020]FIG. 10A provides a high-level flow diagram of a method.

[0021]FIG. 10B provides a high-level flow diagram of a method for designverification

[0022]FIG. 10C provides a high-level flow diagram of a method for maskcorrection

[0023]FIG. 11 describes an application in which designs are modified tomeet desired printed or etched feature dimensions.

[0024]FIG. 12 describes an application in which designs are not modifiedto meet desired printed or etched feature dimensions.

[0025]FIG. 13A describes steps commonly used for layout generation.

[0026]FIG. 13B describes steps commonly used for layout generation whendesign verification is inserted into the design flow.

[0027]FIG. 14A illustrates the steps involved in layout extraction.

[0028]FIG. 14B illustrates a continuation of the steps involved inlayout extraction.

[0029]FIG. 14C illustrates a continuation of the steps involved inlayout extraction.

[0030]FIG. 15 illustrates the relationship between spatial regionsacross the chip and the creation of a layout extraction table.

[0031]FIG. 16 describes a process model component.

[0032]FIG. 17A illustrates the use of product wafers in calibrating atool for a particular recipe.

[0033]FIG. 17B illustrates the use of test wafers in calibrating a toolfor a particular recipe.

[0034]FIG. 18 illustrates how a calibration is used to map layoutfeatures to film thickness variation.

[0035]FIG. 19A illustrates the use of a calibration mapping to predictfilm thickness variation for an IC design.

[0036]FIG. 19B illustrates how wafer-state parameters, such as filmthickness variation, can be used to predict electrical parameters.

[0037]FIG. 20 illustrates steps in a calibration process.

[0038]FIG. 21A illustrates steps in a prediction of full-chiptopography.

[0039]FIG. 21B illustrates a continuation of the steps in prediction ofchip topography.

[0040]FIG. 21C illustrates a continuation of the steps in prediction ofchip topography.

[0041]FIG. 21D illustrates a continuation of the steps in prediction ofchip topography

[0042]FIG. 22A illustrates an overview of a prediction of featuredimensions (e.g. line widths) resulting from lithography process stepsor flows.

[0043]FIG. 22B illustrates a mapping provided by a etch predictioncomponent.

[0044]FIG. 23 illustrates a mapping provided by a lithography predictioncomponent.

[0045]FIG. 24 illustrates steps in generating a feature dimensionvariation prediction with regard to variation in chip topography.

[0046]FIG. 25 illustrates steps in generating a feature dimensionvariation prediction with regard to variation in chip feature density.

[0047]FIG. 26A illustrates the use of test wafers to calibrate alithography model to a particular tool and recipe.

[0048]FIG. 26B illustrates the use of calibrated lithography models topredict feature dimension variation.

[0049]FIG. 27 illustrates steps in using calibrated lithography modelsto predict feature dimension variation.

[0050]FIG. 28 illustrates an overview of a verification and correctioncomponent.

[0051]FIG. 29A illustrates steps in verification option A.

[0052]FIG. 29B illustrates steps in verification option B.

[0053]FIG. 29C illustrates steps in verification option C.

[0054]FIG. 29D illustrates steps in verification option D.

[0055]FIG. 30 illustrates an overview of a correction component.

[0056]FIG. 31 illustrates steps to compute modifications to a layout.

[0057]FIG. 32 illustrates the steps to compute modifications to a layoutusing test wafer data.

[0058]FIG. 33A illustrates a relationship between process modelpredictions of surface topography and a prediction of feature dimensionusing a lithography model component.

[0059]FIG. 33B illustrates a use of errors in predicted versus desireddimensions to modify features in a layout to improve printed featuredimensions.

[0060]FIG. 34A illustrates a process for computing relationships amongfeature width, feature space, density or height.

[0061]FIG. 34B illustrates how surface topography is related to designparameters, such as feature width, feature space and density beforeinput into a lithography model.

[0062]FIG. 34C illustrates how test wafers may be used to computemathematical relationships between feature width, feature space, anddensity for a given height or thickness.

[0063]FIG. 35 illustrates how a process may be used iteratively to domulti-layer verification and correction.

[0064]FIG. 36A illustrates steps in using a lithography test wafer.

[0065]FIG. 36B shows an example of a table relating test waferparameters.

[0066]FIG. 37A shows a stack for a lithography test wafer.

[0067]FIG. 37B shows metal level 1 of a lithography test wafer.

[0068]FIG. 37C shows via level 1 of a lithography test wafer.

[0069]FIG. 37D shows the metal level 2 of a lithography test wafer.

[0070]FIG. 38 illustrates a section of varying line widths and linespaces in metal level 1.

[0071]FIG. 39 illustrates a sub-section of fixed line widths and linespaces in metal level 1.

[0072]FIG. 40 illustrates a same sub-section with varied line widths andline spaces for metal level 2.

[0073]FIG. 41A illustrates patterns in metal level 1 and metal level 2.

[0074]FIG. 41B illustrates metal level 2 superimposed on metal level 1.

[0075]FIG. 42 illustrates varying array structures in metal level 1.

[0076]FIG. 43 illustrates a large array of vias in via level 1.

[0077]FIG. 44A illustrates patterns in metal level 1 and via level 1.

[0078]FIG. 44B illustrates via level 1 pattern superimposed on metallevel 1 pattern.

[0079]FIG. 45A illustrates three areas of slotting structures in metallevel 1.

[0080]FIG. 45B illustrates slotting patterns for three areas in metallevel 1.

[0081]FIG. 45C illustrates a via pattern in the via level 1 superimposedon metal level 1 slotting structures.

[0082]FIG. 45D illustrates a metal level 2 pattern superimposed on vialevel 1 and metal level 1 patterns.

[0083]FIG. 46A illustrates an application of a method to address surfacetopography.

[0084]FIG. 46B depicts an impact of a method when surface topographyoccurs.

[0085]FIG. 47A illustrates an application of a method to address featuredensity.

[0086]FIG. 47B depicts an impact of a method when feature densityoccurs.

[0087]FIG. 48 illustrates an application of a stepper mechanism toaddress wafer-level surface variation.

[0088]FIG. 49 illustrates a stepper mechanism with a proper focaldistance to an alignment key and including imaged areas within the chipthat are outside of the depth of focus

[0089]FIG. 50 illustrates an application of a method to a chip-levelstepper mechanism.

[0090]FIG. 51 illustrates an implementation of a method using computerhardware, software and networking equipment.

[0091]FIG. 52A illustrates an implementation of a method where clientand server reside or are bundled with other software on a singlecomputer.

[0092]FIG. 52B illustrates an implementation of a method where theclient and server communicate via a network.

[0093]FIG. 53 illustrates an implementation of the method where theclient communicates with a server and web services via a network.

[0094]FIG. 54 illustrates an implementation of a method within anelectronic design automation (EDA tool).

[0095]FIG. 55 illustrates a use of the implementation within an EDAtool.

[0096]FIG. 56 illustrates a use of the implementation communicating withan EDA tool via a network.

[0097]FIG. 57 illustrates use of the method within a design formanufacturing system.

[0098]FIG. 58 illustrates use of the method within a design formanufacturing system for choosing lithography related tool settings,recipes or consumable sets.

[0099]FIG. 59 illustrates a GUI for managing layout extractions frommultiple designs.

[0100]FIG. 60A illustrates results from a feature width extraction froma chip layout.

[0101]FIG. 60B illustrates results from extraction binning based uponfeature width.

[0102]FIG. 61 illustrates a GUI for a design for lithography systemembedded within a design for manufacturing system.

[0103]FIG. 62 illustrates a GUI for managing tools and tool recipeswithin a design for lithography or design for manufacturing system.

DETAILED DESCRIPTION

[0104] In what follows, we describe approaches that are useful toidentify and correct, in advance of lithographic mask creation, areas ofan integrated circuit (IC) that are likely to be problematic due tovariations in film thickness, surface topography uniformity, andelectrical impact that arise in the manufacture of an integratedcircuit. The identifications or corrections may be based on predicted ormodeled physical and electrical properties of a manufactured IC, arisingfrom dependencies between predefined circuit layout patterns and thecharacteristics of the processes used in the manufacture of theintegrated circuit.

[0105] These approaches are applicable to (a) high-density plasma (HDP)and chemical-mechanical polishing (CMP) processes used in the formationof shallow trench isolation (STI) structures; (b) lithographic,high-density plasma (HDP), electroplated copper deposition (ECD), andchemical mechanical polishing (CMP) processes used in the formation ofsingle- and multi-level interconnect structures for integrated circuit(IC) devices; (c) processes and flows used to create oxide and low-kdielectric layers; (d) plasma-etch processes and the measurement ofcritical feature dimensions; (e) lithographic process flows that mayinclude pre and post photo resist deposition and removal steps and asubsequent plasma etch step used to physically etch the patternedfeatures into the wafer; (f) photoresist deposition and photoresistmaterial selection, (g) any step or steps in damascene process flows;and (h) computation of corrections to mask dimensions to achieve desiredcritical IC dimensions.

[0106] In fabricating integrated circuits, the degree of interconnectfilm uniformity (in terms of both thickness and surface topography) isdependent on characteristics of circuit layout patterns (e.g. materialdensity, line widths, line spaces, and other feature dimensions).Surface and thickness non-uniformities often lead to subsequentmanufacturability and process integration issues. Pattern dependenciesoften cause processed films to have significant variation. The variationbecomes worse as subsequent non-conformal layers are deposited andpolished.

[0107] An integrated circuit (IC) typically includes multiple levels ofmaterials that have been deposited, planarized, and selectively etchedto reproduce circuitry defined by a computer-generated design.Lithography is a frequently repeated process step during the manufactureof ICs in which a pattern that defines the dimensions of the circuitryis transferred to a silicon wafer. The patterns are subsequently usedwith the etch process to physically etch the features into the wafersurface or other thin films deposited on the wafer surface. The termsfeature dimensions or feature size refer to dimensions of the geometrieswithin the circuit. Examples include: the width of a line, the spacingbetween structures (e.g. the spacing between two lines in an array oflines or a buffer distance between working circuitry and dummy fillstructures), the critical dimension (CD) of a circuit (i.e. the smallestdimension of any geometry in the circuit), widths of arrays of lines orother repeating structures, as well as the metrics (e.g. minimum,maximum, and average) on individual geometries or on groups ofgeometries (e.g. an array of lines). Feature dimensions may also includevertical and other dimensions, including sidewall angle, feature height(e.g. trench depth). Lithography equipment includes mechanisms (e.g.steppers) used to project images of patterns onto wafers and patterntransfer tools (e.g., masks and reticles) used to transfer circuitrypatterns onto wafers coated with a photosensitive film. Etch equipmentincludes mechanisms to selectively remove materials (e.g. oxide) from awafer surface or thin films on the wafer surface patterned withlithography equipment.

[0108] A basic projection lithography process is illustrated in FIG. 1.A light source (e.g., a lamp or laser) 10 is used to project light 12through a condenser lens 14, which directs light through a mask orreticle 16 that contains a pattern that represents the printed circuitfeatures. The light 12 then passes through a reduction lens, whichfocuses the image onto a wafer 22. The minimum feature size that can beimaged can be defined using the Rayleigh equations as:$M_{fs} = {k_{1}\frac{\lambda}{NA}}$

[0109] where λ is the exposing wavelength and NA is the numericalaperture of the optics. The parameter k₁, normally between 0.65 and 0.4for deep ultraviolet (DUV) imaging systems, is a process and systemdependent variable that includes effects such as resist, processimprovements, light source, and reticle characteristics.

[0110]FIG. 2 describes the process of how a lithography mask may becreated from an IC design. A computer-aided-design (CAD) system 36 isused to translate a functional circuit design to an electronic layoutdesign file that represents a physical device, layer-by-layer. Theresult is a design layout that describes each level of the device fromthe lowest level, for example a transistor level, up to higher levels,for example interconnect layers that transmit signals among transistorsand supply power to the components on the chip. The electronic designfiles are used during so-called tape-out to generate specifications formaking a mask 37. The masks are then manufactured 38 and used with thelithography tool to transfer circuit features to a wafer 39.

[0111] Many projection systems use step-and-repeat mechanisms thatexpose only a sub-area of the wafer or a die, also referred to as theoptical field, and then repeat the process until the entire wafer isimaged. The stepper may be controlled to accommodate wafer-levelvariation that occurs across the wafer as a result of, for example, warpor bow. This is normally used to accommodate variability that occursfrom die to die, but not variability that occurs within each die. Toensure that the printed circuit is within a depth-of-focus associatedwith the optics, the stepper may adjust the focal length of the opticsbased on measurements of test keys or alignment marks, which are formedon a surface of the wafer, to accommodate variation in the thickness ofthe photosensitive film or photoresist. Underlying film thicknessvariation in materials below the photoresist often causes the variation.

[0112]FIG. 3 illustrates that while the stepper can account fordie-to-die variation, it may not adequately address within-die variationcaused by IC pattern dependencies. The reduction lens 18 of FIG. 1 isshown above the die surface 30 in FIG. 3. The projection system adjustsso that the focal length 24 matches the measured distance to a test keyor alignment mark 26. The depth of focus 28 determines what featuresalong the optical axis can be reproduced with the desired resolutionM_(fs). Using the Rayleigh equations, depth of focus D_(f) 28 can beexpressed as: $D_{f} = {{\pm k_{2}}\frac{\lambda}{({NA})^{2}}}$

[0113] where λ is the exposing wavelength and NA is the numericalaperture of the optics. The parameter k₂ (normally around one for deepultraviolet or DUV imaging systems) is a scaling factor based uponprocess related characteristics. During deposition of copper materialvia ECD or through the CMP of oxide or copper, for example, processrelated pattern dependencies often cause within-die variation 30 acrossthe chip. If the chip-level variation exceeds the depth of focus, thenthe printed features 32 may not accurately represent the criticaldimensions of the IC design as patterned on the mask and the errors, asimaged on the wafer, may negatively impact the performance of thedevice. As explained below, it is possible to adapt the mask design sothat the printed IC dimensions better match the designed dimensions.

[0114] The next few paragraphs describe the cause and result ofprocess-related IC pattern dependencies.

[0115] The lithography process is repeated throughout the manufacture ofa semiconductor device as each subsequent layer is created. One areawhere the techniques described here may be particularly helpful isduring a damascene process in which metal lines, that connect devicecomponents (called interconnect), are created. Multiple layers ofconnections are used to transmit signals and power among devicecomponents.

[0116] The damascene process flow for a given interconnect layer isdescribed in FIG. 4. The flow begins with a post-CMP planarized surface40 of the prior interconnect level (level N−1). A dielectric material(e.g. oxide or low-k material) is deposited 42 to electrically isolatethe previous and current interconnect layers N−1 and N. (The dielectricforms what is called an inter-level dielectric or ILD layer. Althoughpattern dependencies due to underlying features may require a CMPplanarization step on the ILD, that step is optional and is not shown inthis flow example.) A photosensitive film (e.g. photoresist) isdeposited on the ILD wafer surface 44. A lithography system images thewafer 46 to define circuit features for the current interconnect layerusing a process similar to that illustrated in FIG. 1. A developer isused to selectively remove photoresist 48. Plasma etch is used to removeselective oxide areas 50 and the remaining photoresist is subsequentlyremoved 52. A barrier material is then deposited 54 and subsequently ECDis used to deposit metal, for example copper 56. CMP is used to polishaway selective copper areas and remove the barrier material 58. Thiscompletes the formation of metal interconnects for level N. Oftenpattern-related non-uniformity is transferred from underlying levels tooverlying interconnect levels resulting in variations in the ILD andphotoresist thickness that is imaged during lithography.

[0117] As described in FIG. 5, electroplated copper deposition (ECD) isa process step in a copper damascene flow that is used to deposit coppermaterial within the interconnect structures. The goal is to completelyfill an etched trench region in a void-free manner while minimizing avariation in the deposited copper thickness and minimizing a variationin surface topography. There exist pattern-dependencies in ECD thatresult in plated surface variation. FIG. 5 shows, for example, thedifference in post-plated thickness T_(diff 84) commonly observedbetween the deposited copper thickness T_(narrow) 70 that occurs overnarrow line widths 72 and the deposited copper thickness T_(wide) 82that occurs over a wide line width or trench 86.

[0118] Film thickness variation in chemical mechanical polishing (CMP)processes can be separated into various components: lot-to-lot,wafer-to-wafer, wafer-level, and die-level. Often, the most significantcomponent is the pattern dependent die-level component. Die-level filmthickness variation is often due to differences in layout patterns onthe chip. For example, in the CMP process, differences in the underlyingmetal pattern result in large long-range variation in the post CMP filmthickness, even though a locally planar surface topography is achieved.This variation occurs in copper, oxide, and shallow trench isolation(STI) CMP and is described in following figures.

[0119] For oxide polishing, the major source of variation is caused bywithin-die pattern density variation 102, shown as two groups of metallines in FIG. 6A. The metal lines 106 on the left side of FIG. 6A have alower density in the direction of the plane of the integrated circuitthan do the metal lines 108 on the right side of the figure. Patterndensity, in this case, is defined as the ratio of raised oxide area 110divided by the total area of the region. The region may be taken as asquare with the length of the sides equal to some length, for example,the planarization length. The planarization length is usually determinedby process factors such as the type of polishing pad, CMP tool, slurrychemistry, etc.

[0120]FIG. 7A illustrates an example of how the underlying featuredensity affects the film thickness variation. FIG. 7B plots the filmthickness variation corresponding to each density type. For a givensquare area defined by planarization length 132, the higher underlyingfeature density leads to larger film thickness variation 134. The lowerunderlying feature density leads to a reduced film thickness 135.Designers often try to maintain density tightly around 50% 133 topromote planarity. The effective pattern density may be computed foreach location on the die by filtering the designed layout densities,often by using various two-dimensional filters of densities around thegiven location. FIG. 6A illustrates how the underlying features 106 and108 cause variation in local surface topography (step height) 104 andglobal non-planarity 102.

[0121] In creating shallow trench isolation (STI) structures (examplesare shown in FIG. 6B), SiO₂ 112 is deposited in a trench etched insilicon 111 and planarized using CMP to electrically isolate devices. Aswith oxide inter-level dielectric (ILD) polishing, the underlyingpattern of isolated trenches results in unwanted variation in thedeposited SiO₂. Problematic areas often are created as a result of CMPsuch as nitride erosion 114 (where the nitride barrier is removed andpossibly exposes the underlying Si to contaminants and damage), cornerrounding 116 and oxide dishing 118. The corner rounding has the effectof potentially widening the trench and where the exposure of Si 110destroys the device. The oxide dishing results in topography variationthat impacts subsequent lithography. In STI polishing, pattern densityis an important feature with regard to topographical variation and otherCMP effects.

[0122]FIG. 6C illustrates the effects of polishing metal features (e.g.,copper lines 122 and 126) entrenched in a dielectric (e.g., SiO₂) 120,during a damascene CMP process. For metal polishing, computation ofpattern density is important to characterizing full-chip patterndependencies; however determining other physical layout effects, such asthe line width and line space, may also be required. Two unwantedeffects known as dishing and erosion result from metal damascene CMP.Dishing 124 is measured as the difference in metal thickness at the edgeof a line and its center. Erosion 128 is defined as the difference inoxide thickness above a metal line, typically within an array of lines,to the oxide thickness in an adjacent unpatterned region. Anotherunwanted effect is residual copper 130 that is has not been removed fromdielectric field (or up areas) of the chip and remains on the waferafter polishing is complete. It is common for process engineers to setpolish times such that all residual copper is removed. For thosepatterned areas where copper is cleared first, dishing and erosioncontinue to occur, thereby increasing the non-uniformity of the wafersurface. Each of the described CMP processes contribute to surface levelnon-uniformity and thus may negatively impact lithography. While thetechniques described here are applicable to any process related patterndependencies, ECD and CMP are two processes that cause specific concernregarding non-uniformity. Although these processes will be used toillustrate the methods, the methods are applicable to patterndependencies related to any process.

[0123] The impact of process related pattern dependency on lithographyis illustrated in FIG. 8. For the sake of clarity, the mask 184 andwafer 192 are shown and the related optics are not shown. As a matter ofterminology used throughout, feature width (FW) is taken to be thesmallest dimension of any given object. This term encompasses varioustypes of layout objects, Such as lines, rectangles, polygons, etc. Also,the critical dimension (CD) is understood to be the smallest dimensionof any feature on the layout, i.e. the smallest FW.

[0124] A mask 184 is shown with two features with the same featurewidth, (w), 180 and 182 to be printed onto a wafer surface 192. Whenlithography is performed, the within-die non-uniformity 192 due toprocess-related pattern dependencies (as illustrated in FIGS. 5, 6, and7) may result in a film thickness difference (Δh) 186 across the chipbetween the two printed line widths w₂ 188 and w₁ 190. In this case 194,the printed line width w₁ 190 is much larger than w₂ 188. Although bothline widths 180 and 182 have been designed and created on the mask withthe same dimensions, surface level non-uniformity may result insignificantly different dimensions in the printed features 188 and 190,which subsequently affects the performance of the manufactured IC.

[0125] Process related pattern-dependencies may also occur within thelithography process itself where the density of features often affecthow well the printed features reproduce those designed. In FIG. 9, amask 214 is shown with two sets of features: one with higher density 210and one with lower density 212. As features on the chip are placedcloser to each other (i.e. feature density increases), the diffractionpatterns associated with them change often resulting in a featuredimension that varies from that designed. Even with a perfectly planarwafer surface across the chip 216, the printed feature dimensions (e.g.line widths) (w+Δ1) 218 and (w+Δ2) 219 may vary 220 from the dimensionsdesigned and patterned on the mask.

[0126] Topographical variation may occur over all components within achip and thus full-chip characterization or prediction may be useful. Insome cases, it is useful to focus on critical components or circuitareas call sub-networks or sub-nets. Within this context, full-chipprediction is meant to include any focus on topographical variationwithin a critical sub-net.

[0127] IC pattern dependent relationships can be used to verify whetherfeature dimensions produced by lithography match the dimensions as theywere designed, and, if not, to modify the design layout and masks toyield the designed features. Lithography models may be combined withetch models to predict the physical feature dimensions created withinthe wafer. Electrical extraction and simulator components may also beused to assess the electrical impact of variations in features (e.g.width, height, depth, sidewall angle) across the chip and fine-tune thespecified tolerances for the chip.

[0128] The following paragraphs describe an embodiment of the method,which is depicted in FIG. 10A. Sub-blocks (310, 400, 600 and 800) withinFIG. 10A will be described in greater detail below.

[0129] An IC design is commonly represented electronically, e.g., in aGraphical Data Stream (GDS) format, in a library of files that definestructures and their locations at each level of an integrated circuit280. These files are typically large, although the features that arerelevant to process variation may be described more efficiently. Aprocess of layout extraction 310 involves summarizing discrete grids(sub-portions) of IC designs in a compact set of parameters such asfeature width, feature space, and density for each grid. Layoutextraction is not required but may be helpful where computationresources are constrained. A description of how to perform layoutextraction is described in section a below.

[0130] In the prediction component (P_(r)) 300, the layout features 280of the design are mapped 310 to parameters of wafer topography (Δh) 580,such as film thickness, dishing, erosion, and total copper loss. Thisinformation may be used by a process model (e.g., a CMP model) or a setof process models M_(p) (e.g., ECD and a multi-step CMP process or amore complex process flow) 400 to predict or simulate the manufacturingresults and corresponding variation that will occur when the designrepresented by the layout features is manufactured on the modeledprocess. The variation of the resulting fabricated device can bemeasured physically, such as by optical measurement of the filmthickness or surface profiling of the wafer surface to determine actualtopography (e.g. dishing or step height and erosion or array height).The chip-level surface topography and associated electrical parameters580, relevant for comparison to the desired specifications 750, arecomputed for the full-chip, both within die and for multiple dies acrossthe wafer.

[0131] The predicted chip-level topography 580 is input into alithography modeling M_(L) step 600 that maps the variation in wafersurface height 580 to the variation in printed feature dimensions 680for the particular lithography tool. This mapping may use the toolspecifications and equations for minimum feature size (M_(fs)) and depthof focus (D_(f)) to compute the feature dimension variation with respectto surface topography (as shown in FIG. 8) and an optical proximitycorrection tool (e.g., existing commercial versions) to compute thefeature dimension variation with regard to feature density (as shown inFIG. 9). Another approach is to utilize test wafers and a calibrationprocess described in FIGS. 36A and 36B and section f. to capture patterndependencies with regard to surface topography and feature density. Theresult of these approaches is the predicted variation in featuredimensions and line widths across the full-chip 680 for one or multipledies across a wafer that has been processed using lithography process orflow 680.

[0132] One option is to use models in which the lithography process flow600 is defined to include not only the lithography process step but mayalso include pre and post photoresist deposition and subsequent plasmaetch. This may be useful if the actual physical feature dimensions aredesired, as an alternative to the patterned feature dimensions thatlithography models alone provide. It is recommended to use a patterndependent etch model that provides additional feature dimensions such assidewall angle and trench profiles. This step concludes the predictioncomponent P_(r) 300.

[0133] The predicted feature dimension variation 680 and the desiredfeature dimension specification and tolerances 750 are input into averification and correction component 800 which identifies any featuresthat will exceed or approach the tolerances. This component also may beused to correct the dimensions of the identified features within thedesign layout and in subsequent mask creation so as to achieve thedesigned (or desired) feature dimensions across the chip. Once thesemodifications are made to the IC design, dummy fill may be reinserted oradjusted and a new layout generated.

[0134] Dummy fill is a method of improving film thickness uniformity inintegrated circuits through the addition of the structures or theremoval of existing structures. Adding metal dummy fill increases thepattern density since density is defined as the amount of metal dividedby the total area within a given region. Conversely, adding oxide dummy(also called slotting) removes sections of the copper line and decreasesthe pattern density. The addition of fill can also alter otherparameters such as line width and line space. If dummy metal is insertedbetween two parallel lines, the line space changes for both of thoselines. Similarly, if oxide dummy is inserted within a wire, itseffective line width is changed. By modifying the existing layoutthrough the addition of dummy fill, physical parameters such as patterndensity, line width, and line space are changed.

[0135] The new layout is then input into the prediction component toensure that the new design meets not only the lithography relatedfeature dimension requirements but also the design and electrical rulesand specifications as well. This will likely be an iterative processuntil the criteria are met across all concerns.

[0136]FIG. 10A describes the basic flow for design verification and formask correction. FIGS. 10B and 10C provide more detailed flows fordesign verification and mask correction, respectively. The motivationbehind design verification is to predict feature width and topographicalvariations and to use electrical simulations to verify that a givendesign meets the desired criteria. As such, it is important to modifythe design file to reflect the feature dimensions that will result foreach interconnect level. As shown in FIG. 10B, the first step is togenerate the layout for an interconnect level (e.g. level N). Thefull-chip design, a critical sub-portion of the circuit design or anextraction from the layout is used to predict feature width variation222 due to the lithography (and optionally, plasma etch as well)process. This is similar to the prediction component 300 shown in FIG.10A. The original design file is stored 223 for future use because ifthe design passes verification, the original design will be used tocreate the masks. A temporary design file is modified 224 to reflect thefeature width variation that will result from the lithography (andoptionally, the plasma etch) process. The electrical impact of featurewidth variation can be evaluated 225 by performing full-chip or criticalcircuit network simulation using resistance-capacitance (RC) extractionand other electrical simulation tools. This allows for examination ofissues, related to interconnect feature width variation such as couplingcapacitance, noise and timing. The physical characteristics (e.g. totalcopper loss, dishing and erosion) and electrical characteristics (e.g.sheet rho variation, timing closure, signal integrity, power grid andoverall performance) are checked 226 against specifications for thedevice. The verification step weighs the results and either passes orrejects this design level. If the design passes, the original designfile is used for mask creation 228. If the design is rejected or failsto pass, both the feature width and topographical variation results areprovided to the designer or may be input into a design or maskcorrection component 229, such as the mask correction approach describedhere. Approaches for both design verification and mask correctioncomponents are described in Section e.

[0137] A mask correction technique is shown in FIG. 10C and may beintegrated with an electronic design automation (EDA) tool (as shown inFIGS. 54 and 55) or used separately (FIG. 56). The first step isgenerate the layout for an interconnect level (e.g. level N) 231. Thelayout is normally generated using an EDA tool that places circuitcomponents and routes wiring for interconnect levels. Often dummy fillis added 232 to promote uniformity. The dummy fill may be performed atthis stage or performed during the prediction step in 235 when thetopographical variation due to pattern dependencies is computed. Thenext step 233 is physical verification in which the design is checked tomake sure that it meets all the design rules and parameters that arespecified by manufacturing (e.g., a foundry). Physical verification isoften part of the normal EDA tool flow that includes steps 231, 232, 233and electrical simulation 234. Normally optical proximity correction(OPC) is done, as part of physical verification, to adapt features tocompensate for sub-wavelength distortions. However it is recommendedthat this component be made inactive in any design flow and that OPCmethods be used in step 235 instead. If both are used, then the designis adapted for mask creation before the topographical effects onlithography can be properly evaluated. The next recommended step iselectrical simulation, which is used to verify that the feature widths,as designed, meet the electrical specifications 234. The full-chipdesign, a sub-network of the circuit or an extraction from the designlayout is then input into the feature width prediction component thatcharacterizes the impact of pattern dependencies on the lithographyprocess (and optionally, the etch process as well) 235. This is similarto the prediction component 300 shown in FIG. 10A. Optical proximitycorrection (OPC) 236 may be performed within the prediction step, asshown in 640 FIG. 22A, or separately, as shown in 236, using an existingcommercial tool. The next step is correction 237 where the design fileis modified so that the mask features compensate for width variation. Itis recommended that any modifications to the design files 237 by thesecomponents (235 and 236) be coordinated. These steps may be repeated 230for each interconnect level until the highest interconnect level isreached. When modifications to design files, to be used for masktape-out for each interconnect level, are complete, the electronic filesare sent out for creating the masks. It is important to maintainseparate design files though. The design files that have been modifiedto compensate for the width variation are only useful for mask creation.The masks if properly modified will result in feature dimensions thatclosely resemble those designed in the original design files. As such,any further simulation or analysis should use the original design files,whose dimensions will be accurately represented in the manufacturedcircuit.

[0138] Two examples of how the techniques may be applied to damasceneprocess flows are provided in FIGS. 11 and 12, which will be referred toas modes A and B respectively. The damascene process flow is a goodexample because non-uniformity may propagate from level 1 to level 2 andso on until the final level N is reached, and the following figuresillustrate the iterative nature of the approach. To simplify the processflow descriptions, pre and post wafer treatments that do notsignificantly affect wafer topography are ignored. Also, to simplify theexample to a generic damascene flow, the term interconnect level is usedas a global reference to include both metal and via levels; anyadditional oxide deposition or etch steps to form vias are not shown.The damascene flows illustrated can be easily extended to dual-damasceneand other damascene process flows. Also, the process flows shown inFIGS. 11 and 12 are for the case where plasma etch is not included inthe lithography process module 600 and is computed separately. If theoption to predict etched or physically created feature dimensions isused, the etch model 250 is used within a lithography process flowcomponent 600 before comparison 246 or modification 260.

[0139] The difference between the two approaches is that in mode A, thedesign is modified before mask creation and tape-out to produce thedesired dimensions and thus the original design and extraction reflectthe actual printed circuit dimensions (if one uses the corrections tothe mask to produce the originally designed features). The layoutextraction for the original design still reflects the processed featuredimensions or may be close enough to assume the designed widths are usedin subsequent ECD process steps.

[0140] In mode B, the design is modified to reflect the impact of widthvariation due to lithography. The variation in feature dimensions ateach level needs to be reflected in subsequent steps that have patterndependencies. As such, the design file is adapted, another layoutextraction may be performed and the variation is propagated to the nextinterconnect level to examine multi-layer effects.

[0141] Mode A is oriented toward mask correction to yield minimalfeature size variation. Mode B is useful for characterizing lithographyprocess impact, for a given design, within the flow. This is also usefulin determining measurement plans for feature dimension variationimpact—perhaps for existing production device flows where the masks havealready been made and being used in production. As such, the full-chipfeature dimension variation has to be taken into consideration forsubsequent process impact and the design appropriately modified togenerate a new layout extraction for downstream process prediction. Alsoif the full physical and electrical impact of lithography variation isto be examined the changes to feature dimensions should be modifiedbefore simulation (perhaps using RC extractor or EDA tool) as well. Thatallows for the electrical impact of lithography variation to becharacterized as well.

[0142]FIG. 11 describes mode A in which the design is modified to yieldminimal feature dimension variation after each lithography prediction.Please note that further details on each step will be provided insubsequent sections and these descriptions are to indicate the flow andoperation of the components in FIG. 10.

[0143] The sample application begins with interconnect level 1, thelayout is generated 280 for levels 1 through the final level N, theprocess model component 401 is used to extract layout parameters 240,and the ILD process model 242 is used to predict the full-chipdielectric thickness, also referred to as Δh in FIG. 10. The lithographymodel component 600 is used to predict the feature dimension variationΔFW. One option is to import feature width variation to electricalsimulation tools to characterize the electrical impact and transfer theelectrical characterization of feature width variation to theverification component 246 as well.

[0144] The verification component 246 compares the prediction andspecifications and identifies problematic areas. The correctioncomponent 248 modifies the design so that the lithography process yieldsthe desired feature dimension levels. Since the printed features nowmatch (or are sufficiently close within some acceptable threshold) theoriginal layout extraction parameters 240, a new layout extraction isprobably not required unless the feature specifications have been settoo broad. This is a way in which the techniques may be used to modifydesign rules to be less conservative, once lithography variation hasbeen minimized.

[0145] To generate the lithography prediction for interconnect level 2,the underlying topography for all the process steps between the twolithography steps should be addressed. To compute the incoming wafertopography Δh for level 2, the prediction component M_(p) Level 2 402must use the predicted ILD topography from 242, the etch modelprediction 250, the ECD model predicted wafer topography, and the CMPmodel predicted topography 252 from interconnect level 1 and thesubsequent ILD topography 256 from interconnect level 2. The patternthat is imaged during interconnect level 2 lithography is the level 2design, which is extracted 254 and input into the lithography model.Finally, the feed-forward propagation through the model flow yields theincoming topographical variation 256 that is input into the lithographymodel along with the level 2 extraction parameters 254 for predictingthe interconnect level 2 feature variation 600.

[0146] One option for the use outlined in FIG. 11 is to transfer featurewidth variation computed in 600 and 250 and the topographical variationcomputed in 252 into electrical simulations to characterize theelectrical performance for interconnect level 1 and this may be repeatedfor each interconnect level.

[0147]FIG. 12 describes mode B. The mode B approach may be used todetermine the impact of chip and wafer level pattern dependencies on thelithography process for multiple interconnect levels or the entire chip.In this approach, the printed or etched feature dimensions that resultfrom a lithography process flow may not be the same as the desiredfeature dimensions and as such any pattern dependencies in subsequentprocess steps would be based on the printed or etched dimensions. Giventhat circuit dimensions may be significantly different, it isrecommended that the design or extraction be updated to the predictedvariation. When the design is updated to reflect the variation, anotherextraction may need to be performed and forwarded to subsequent modelprediction steps. Further details on each step will be provided insubsequent sections and this description is to indicate the flow andoperation of the components in FIG. 10. The key difference in the stepsdescribed in FIG. 11 and FIG. 12 is that in FIG. 12 the lithographymodel prediction of feature dimension variation 600 is used to modifythe layout 260 so that it accurately represents the full-chip printedfeature width that will actually be printed on the wafer surface. Theexisting extraction may be modified or a new extraction 262 may be runand fed into the subsequent etch process step 250. In the option whereetch models are used within lithography process flow in 600, theresulting variation in features are used to update the layout and a newextraction is ran and fed into the subsequent ECD step 252. Theverification, mode B, operation may be used with existing process flowsto determine measurement and sampling plans to measure problematic areaswhere feature dimension variation is a concern.

[0148] An option for the method in FIG. 10 is to add an electricalextraction or simulation component to predict the resistance,capacitance and overall electrical impact of the feature dimensionvariation that results from lithography,a lithography process flowincluding etch. One may also use this invention for full interconnectlevel electrical characterization by combining predicted feature widthand topographical variation that occurs subsequent ECD or CMP steps andproviding this information to electrical extraction or simulation tools.

[0149] To evaluate electrical impact in FIG. 11, the feature widthvariation computed in 600 and the topographical variation computed insubsequent process steps 252 may be imported into electrical simulationsto characterize the electrical performance for interconnect level 1 andthis may be repeated for each interconnect level.

[0150] To evaluate electrical impact in FIG. 12, the feature widthvariation computed in 600 may be examined and transferred to theverification component in 246 and 250 and the topographical variationcomputed in 252 may be imported into electrical simulations tocharacterize the electrical performance for interconnect level 1 andthis may be repeated for each interconnect level.

[0151] In the final verification pass for a given IC design acombination of both process models and electrical simulations may beused to gauge the performance of a given IC design and compare theprediction against the desired wafer quality and electrical parametersas well as design rule criteria 800.

[0152] Illustrative embodiments are described in the following sections:Section a. describes the layout generation process. Section b. describesthe extraction of layout parameters related to process variation as amethod to transform the large design files into a manageable set offeatures. Layout extraction is not required but is useful. Section c.describes a desirable use of process and electrical models tocharacterize the impact of pattern dependencies and process variation onchip-level topography. Section d. describes the mapping of wafertopography and designed (or desired) circuit features to predictedfeature dimension variation that results from a lithography processflow. Section e. describes the verification process of comparingpredicted and desired feature dimension values across the full-chip anda correction process for modifying design features and generating newGDS design files for mask tape-out and creation. Section f. describesthe creation and use of test wafers to characterize pattern dependenciesassociated with lithography process flows. Section g. describesapplications using the procedures described in sections b. through f.Section h. describes the construction and computational framework usedto implement the methods and the applications described in Section g.,as well as the operation of the system and methods by users.

[0153] a. Layout Generation

[0154] Depending on how the techniques is used (for example, as shown in10B or 10C), the lithography prediction may be used within an EDA designflow, as shown in FIG. 55, or in series with an EDA design flow, asshown in FIG. 56.

[0155] In both FIG. 11 and FIG. 12, the lithography modeling may comebefore or after the layout extraction component. Generally, layoutdesign files are sent through an OPC correction step resulting in thecreation of a post OPC layout design file. The OPC correction may eitherbe rule based or model based, but in either case the layout design fileis modified from its original form in order that the lines actuallyprinted on the wafer surface after passing through the optics of thelithography process most closely represent what was originally intended.In FIG. 29C, verification is performed at the designed featureresolution and no abstraction of the features, using layout extraction,is needed. As such, this is a case where lithography variation ischaracterized and perhaps corrected at the feature dimension resolution.

[0156] The layout extraction component must be performed on a pre OPCdesign file and account for any possible errors that the OPC correctionmay fail to account for, or, if the layout extraction is performed onthe post OPC design file, it must remove the effects of the OPCcorrection in order that it most closely represents what will actuallybe printed on the wafer surface.

[0157] If one is to utilize the lithography model component for OPC andrely on its ability to change the GDS design file such that you get whatis designed into the GDS file, then modifications based on topographyvariations due to CMP may also be moved up above the lithographymodeling/OPC block.

[0158] In other words, if the techniques are integrated within an EDAtool, any modification of feature widths are to be made before OPC, sothat the OPC tool could insert and adjust changes to the GDS file (in itnormal operating fashion). Alternatively, the topographical variations(Δh) could just be forwarded into the OPC tool and it could adjust forboth the surface variations and the optical proximity. All of these areoptions, depending on how the techniques are to be used and whether itis used with an EDA tool and OPC component or not.

[0159] Two such ways of generating process layouts (or electronic designfiles) are described in FIG. 13A and FIG. 13B. FIG. 13A describes amethod of correcting masks for a layout generated in a design flow,typically performed using an EDA tool. Layout generation 280 describesthe process that converts a functional circuit design to a layout. An ICdesign is commonly represented electronically in a layout design file(e.g., in a Graphical Data Stream or GDS format) in a library of filesthat define structures and their locations at each level of anintegrated circuit. The process begins with a layout of where majorcomponents (blocks of circuitry) are located on the physical die 282.Place and route 284 is then done to determine precisely where every cellor block is positioned and how all components are connected. Dummy filladdition 286 may be performed to modify the density of materials in agiven layer, while minimizing the electrical impact (Additionalinformation concerning dummy fill is set forth in U.S. patentapplication Ser. Nos. 10/165,214, 10/164,844, 10/164,847, and10/164,842, all filed Jun. 7, 2002). Dummy fill may also be performedlater after topographical variation is characterized as part of theprediction component 300. The next step 288 is physical verification inwhich the design is checked to make sure that it meets all the designrules and parameters that are specified by manufacturing (e.g., afoundry).

[0160] A common option, during or after the physical verification stepin a design flow, is to pass the design through optical proximitycorrection (OPC) to adapt the design file used to create masks withregard to feature density. Within the methods described here, the stepmay be performed in the lithography modeling component 600 so thatmanufacturing variation may be taken into account along with featuredensity.

[0161] Often electrical extraction and simulation are performed 290 toverify that the chip, as verified in the prior step and with dummy filladded, meets electrical performance requirements. Within the context ofthe methods described here, electrical impact also includes full-chipprediction of sheet resistance, total copper loss, capacitance, drivecurrent and timing closure parameters.

[0162] The design modifications are generated in a layout design fileformat and assembled into a library. To achieve a smaller electronicfile size, a hierarchical method may be used to compress the size of thedesign files (Such a hierarchical method is described in U.S. patentapplication Ser. Nos. 10/165,214, 10/164,844, 10/164,847, and10/164,842, all filed Jun. 7, 2002.). Once layout generation iscompleted, the design may be input into the layout extraction component310. The layout extraction, the actual full-chip design at the featureresolution or some portion of the circuit such as a critical network isfed into the prediction component 300.

[0163] The layout generation process described in FIG. 13B thegeneration and verification of a design. The components are the same asdescribed in FIG. 13A and the prior paragraphs in this section. Howeverthe order is different so that the physical and electrical impact offeature width variation may be inserted into the design processdirectly. The process in FIG. 13B is similar to that of FIG. 13A in thatit begins with a layout of where major components (blocks of circuitry)are located on the physical die 282. Place and route 284 is then done todetermine precisely where every cell or block is positioned and how allcomponents are connected. Dummy fill addition 286 may be performed tomodify the density of materials in a given layer, while minimizing theelectrical impact (Additional information concerning dummy fill is setforth in U.S. patent application Ser. Nos. 10/165,214, 10/164,844,10/164,847, and 10/164,842, all filed Jun. 7, 2002). Dummy fill may alsobe performed later after topographical variation is characterized aspart of the prediction component 300. The next step 288 is physicalverification in which the design is checked to make sure that it meetsall the design rules and parameters that are specified by manufacturing(e.g., a foundry).

[0164] In this mode, the techniques described here work with thephysical verification component and may, as shown later in FIG. 54 andFIG. 55, be directly embedded or integrated within a physicalverification component within an EDA tool. In some cases where thecomputational burden is a constraint, a layout extraction may beperformed (described in more detail in Section b.) 310. In other cases,the actual design file or some portion of the circuit (e.g. a criticalsub-network) may be directly imported into the physical verification 288and prediction components 300.

[0165] The prediction component examines and characterizes feature widthvariation 300 and updates a design file, which reflects the variation inmanufactured circuit if the masks use the original layout produced in280. The electrical impact of this variation on circuit performance maybe evaluated by using electrical extractions and simulations that areperformed 290 to verify that the chip meets electrical performancerequirements. Within the context of the methods described here,electrical impact also includes full-chip prediction of sheetresistance, total copper loss, capacitance, drive current and timingclosure parameters. The overall impact of feature width variation onphysical and electrical characteristics for the interconnect level areevaluated against desired device specifications.

[0166] In later figures and descriptions, layout generation willindicated with a ‘L’ and may include any and all of the cases discussedin this section but is not limited to the two cases described in FIG.13A and FIG. 13B.

[0167] b. Layout Parameter Extraction

[0168] As described in section a., a layout is a set of electronic filesthat store the spatial locations of structures and geometries thatcomprise each layer of an integrated circuit. It is known that variationduring manufacturing, which negatively impacts the chip-level planarityof processed films, is related to the variation in spatial densities andthe spatial distribution of features within a given design. Thisrelationship may be characterized using layout extraction, in whichcharacteristics of the feature layout (e.g. width and spaces of linesand pattern density) are extracted spatially across a chip from thegeometric descriptions in layout files. The extracted information maythen be used to determine areas of the chip that exceed design rulecriteria, such as limits on feature dimensions and distances toneighboring structures.

[0169] The layout parameter most often used to compute dummy fill is theeffective pattern density. Although the dummy fill method works withextracted densities, it is useful to include the extracted featurewidths and spaces. Since lithography impact must take into considerationall features, whether electrically active or dummy structures, it isrecommended to use designs with dummy fill added and the associatedlayout parameters for purposes of layout extraction.

[0170] The flowchart in FIGS. 14A, 14B and 14C provides a detailed flowof the layout extraction component 310 of FIG. 10. The layout file istransferred or uploaded to the computer where the extraction algorithmis running 311. The layout is divided into discrete grids, small enoughso that aggregate computations of mean, maximum, and minimum featurescan be used to represent the structures in the grid and still allowaccurate feature representation 312. The trade-off is between higher andlower grid resolution is the increased extraction, calibration, andprediction compute times versus a more faithful representation of thelayout and more accurate predictions. It is recommended to use a gridsize that is less than feature dimensions; however section e. and FIG.29A presents a method for using larger grid sizes such as 40 μm×40 μmfor verification and correction. The grids are ordered or queued forprocessing 313. One desirable approach is to use multiple processors tocompute the grids in parallel 314. A grid is selected 315 and withinthat grid the width of each object 316 is computed 317. This process isrepeated for every object within that grid 318. For each set ofneighboring objects (e.g. adjacent objects or objects within somedefined distance of an object in being processed) the maximum, minimum,and mean space is computed 319. The effective density for the entiregrid is then computed 320. This process is repeated for all theremaining grids 321. Once all the grids are processed, the extractedfeatures such as width, space, and density are reassembled from theparallel processors 322.

[0171] A table is then created and the maximum, minimum, and mean width,space, and density for each grid are placed in it as well as themaximum, minimum, and mean width for the whole chip 323. The minimum andmaximum widths for the whole chip are used to compute a range.

[0172] Bins are useful for computing statistical and probabilisticdistributions for layout parameters within the range specified by thebin. The width range (M) for the chip is divided by a number of desiredbins (N) 324 to determine the relative size of each of the N bins. Forexample, the first bin would span from the minimum width or smallnonzero value Δ to the width (M/N). Successive bins would be definedsimilarly up to the N^(th) bin, which will span the width from minFW_(BinN)=(N−1)·(M/N) to max FW_(BinN)=(N)·(M/N), which is also themaximum feature width. The limits for each of these bins may also be setmanually by the user. There are three sets of bins, a set of bins foreach of maximum, minimum, and mean width. Each grid is placed in theappropriate bins according to its max, min, and mean width 325. Ahistogram is also created for each bin showing the distribution ofvalues within that bin 326. This information is stored in the databaseand fed into process models 327.

[0173] The maximum, minimum, and mean feature space ranges are computedfor the full chip 328. The space range (M) is divided by the number ofdesired bins (N) 329 to determine the relative size of each of the Nbins. For example, the first bin would span from the minimum space orsmall nonzero value Δ to the space (M/N) and successive bins would bedefined similarly up to the N^(th) bin, which will span the space frommin FS_(BinN)=(N−1)·(M/N) to max FS_(BinN)=(N)·(M/N), which is also themaximum space. The limits for these bins may also be set manually by theuser. There are three sets of bins, a set of bins for each of maximum,minimum, and mean feature space for the full chip. Each grid isseparated into the appropriate bins according to its max, min, and meanspace 330. A histogram is also created for each bin showing thedistribution of values within that bin 331. This information is storedin the database and fed into process models.

[0174] The density range is computed for the full chip 333. The densityrange (M) is divided by the number of desired bins (N) 334 to determinethe relative size of each of the N bins. For example the first bin wouldrange from the minimum density or small nonzero value Δ to the densityvalue (M/N) and other bins would be defined similarly up to the Nth binwhich will span the density from min FD_(BinN)=(N−1)·(M/N)+Δ to maxFD_(BinN)=(N)·(M/N), which is also the maximum density. The limits forthese bins may also be set manually by the user. There is one set ofbins for density. Each grid is assigned to the appropriate binsaccording to its density 335. A histogram is also created for each binshowing the distribution of values within that bin 336. This informationis stored in the database and fed into process models 337. Finally allthe width, space, and density information 338 are stored either in thedatabase or on the file system for later use in process model prediction400, 600, and 800.

[0175]FIG. 15 provides an illustration of how an extraction table 362(for all the grids across the full-chip or die) is generated using theprocess described in FIGS. 14A, 14B and 14C. The chip or die 360 issegmented into discrete grids 364 and the extraction procedure,described in FIG. 13, is used to compute the width 47 space 48, anddensity 49 for each grid element 46. For each discrete grid on the die364 there exists a feature in the extraction table for the gridcoordinates 366 with the relevant pattern dependent characteristics, forexample density, feature width (FW), and feature space (FS). The figurealso shows an example of two grids with (x, y) coordinates (1,1) 376 and(2,1) 378 and how they may appear in the extraction table. FIG. 13indicates how these characteristics, feature width (FW) 368, featurespace (FS) 370, and density 372 values, may be placed in an extractiontable 362. In many cases, the max, min, and mean of the features withineach grid are stored in the table as well.

[0176] c. Pattern-Dependent Process Models

[0177] A process model or a series of models (e.g., a model of a flow)can be used to predict the manufactured variation in physical andelectrical parameters of an actual IC device from an IC design. Bycharacterizing the process variation relative to IC structures using themodel, variations in topography across the chip may be predicted andused to estimate printed feature size variation during lithography orphysical feature dimensions that result from use of lithography and etchprocessing.

[0178] As described in FIG. 16, pattern-dependent process models andmodel flows 540 are used to map extracted IC patterns andcharacteristics 310 to chip-level topographic variation across the chip580. Each process tool generally has unique characteristics and thus amodel typically needs to be calibrated to a particular recipe and tool500. As such, the pattern-dependent model component 400 includes thecalibration step 500 and the feed-forward prediction step 540. Full-chipor partial chip predictions may include copper thickness, dishing,erosion or electrical impact of topographical variation. The followingparagraphs describe the calibration step 500.

[0179] It is common practice to physically process integrated circuitsin accordance with a given IC design to determine the impact ofprocessing on physical and electrical parameters and to develop orcalibrate process models specific to a particular tool or recipe, asshown in FIG. 17A. In the calibration process 500 shown in FIG. 17A, theactual product wafer 464 is processed using a recipe 465 on a particulartool 466. Pre-process wafer measurements 467 and post-process wafermeasurements 468 are used to fit model parameters 469. A semi-empiricalmodel is used to characterize pattern dependencies in the given process.The calibration model parameters or fitting parameters 470 may beextracted using any number of computational methods such as regression,nonlinear optimization or learning algorithms (e.g. neural networks).The result is a model that is calibrated to the particular tool for agiven recipe 471. In other words, it is a model that, for the particulartool and recipe, is useful in predicting the characteristics of finishedICs that are processed according to a particular chip design.

[0180] Certain IC characteristics, such as feature density, width, andspacing are directly related to variation in topography for plating,deposition, and CMP processes. Test wafers that vary these featuresthroughout some range across the die can be used to build a mapping fromdesign parameters (e.g. width, space, density) to manufacturingvariation (e.g. film thickness, total copper loss, dishing and erosion)for a given tool and recipe. Test wafers are an attractive alternativefor assessing process impact than actual designed wafers because theyare generally less expensive to manufacture and one test wafer designcan be used to characterize any number of processes or recipes for awide range of IC designs. As shown in FIG. 17B, a test wafer 390 can bealso be used to generate a calibrated process model or multiple processmodels or a process flow. The calibration model parameters may becomputed similarly to the method shown in FIG. 17A. One difference isthat the pre-process measurement, 474, may be conducted by the testwafer manufacturer and retrieved in an electronic form, such as via theinternet, email, disc or CD, or in paper form. Another difference isthat the resulting calibration 478 normally spans a much larger range offeature width, spacing, and density, and thus is more applicable to abroad range of devices that could be fabricated on the tool using therecipe. Since a test wafer is normally designed to span a large designspace, the calibration process described in FIG. 17B is recommended.

[0181] More details regarding the use of test wafers in calibrating aprocess are provided in FIG. 18. A test wafer die 479 is patterned witha range of line width and line space values 480. The test wafer isprocessed (e.g., by CMP, ECD, or deposition) on a particular tool usinga given recipe 481 and the resulting variation in a parameter ismeasured across the chip 483 using a metrology tool (e.g. filmthickness, 484). This mapping 482, dictated by the calibration modelparameters, may be considered a model that maps a wide range of linewidth and line space values to a particular film thickness variation forthis tool and recipe.

[0182] These mappings are useful for predicting process variation fornew IC designs, as shown in FIG. 19A. Feature widths and spaces thatfall within the range 486 spanned by the test die and wafer areextracted 485 from a new IC layout. The extracted feature widths andspaces for spatial locations across the chip 486 are input into themapping 487 and an accurate prediction of film thickness variationacross the chip 489 and 490 can be acquired for a given tool and a givenrecipe before processing of the new IC design.

[0183] As shown in FIG. 19B, the predicted process variation 491 (whichmay include variation due to lithography) can be fed into electricalmodels or simulations 492 to assess the impact of processing on theelectrical performance of the chip 493. Some of the electricalparameters that may be computed using the models include variation insheet resistance, line resistance, capacitance, interconnect RC delay,voltage drop, drive current loss, dielectric constant, signal integrity,IR drop or cross-talk noise. These predictions can be used to determinethe impact of feature dimension variation on electrical performance forthe full-chip or critical networks (also called critical nets).

[0184] The following paragraphs and figure descriptions provide adetailed flow of the use of process and electrical models tocharacterize variation, as implemented for lithography.

[0185]FIG. 20 describes the steps involved in calibrating a processmodel to a particular tool or recipe. Layout extraction 310 parametersare computed, or in the case of test wafers, uploaded from the waferprovider. The second step 501 pre-measures the wafer using metrologyequipment. These measurements may include film thickness andprofilometry scans to acquire array and step heights. The test wafer isprocessed 502 using the particular process or process flow that is to becharacterized. Such processes or flows may include plating, deposition,and/or polishing steps. It is particularly useful to calibrate onindividual processes and also to calibrate on sections of the flow as away to capture any coupling of variation between subsequent processsteps in a flow. It is also recommended to calibrate the model fordifferent recipe parameters such as time. The processed wafers aremeasured 503 at the same locations as the pre-measurements; suchmeasurements may include film thickness, profilometry, or electricalcharacteristics; and the variation for the given process may becharacterized 504. Process models or representations are uploaded in 505and the pre and post measurements as well as computed variation may beused to calibrate or fit the model or representation to a particulartool and/or recipe or recipes. These models may be formulated anduploaded by a user or selected from a library of models on a modelingcomputer system. The pre- and post-processing measurements and computedprocess variation are used to fit the model or simulation parameters forthe given tool and recipe 506. The result 507 is a process modelcalibrated to a particular tool and recipe or recipes. The result mayalso include a series of calibrated process models that can be used tosimulate a process flow. The calibration model parameters for specificmodels (e.g. ECD, etch, and CMP), tools, recipes and flows are loadedinto the database and into the models during feed-forward prediction520.

[0186] The steps that constitute the feed-forward prediction component540 are described in FIG. 21A. A damascene process flow for predictingpre-lithography wafer topography is used to illustrate how a predictionmay work but any process flow or single process step may be substituted.To simplify the process flow descriptions, pre- and post-processingwafer treatments that do not significantly affect wafer topography areignored. Also to simplify the example to a generic damascene flow, theterm interconnect level is used as a global reference to include bothmetal and via levels. Any additional oxide deposition or etch steps toform vias are not shown. The damascene flows illustrated can be easilyextended to dual-damascene and other damascene process flows.

[0187] The extraction 310 is loaded into the prediction component 540.The prediction component then retrieves the incoming wafer topography542. For interconnect levels greater than 1, this is the last processstep from the prior interconnect level. For the first interconnectlevel, either the incoming wafer topography can be predicted usingpattern dependent modeling of component creation or initialized toplanar.

[0188] Both the incoming topography and extracted parameters are loadedinto an ILD process model to predict the resulting wafer surface 544.ILD deposition models may include the use of oxide (SiO₂) or low-kmaterial. It is recommended to include pattern-dependencies to acquirefull-chip prediction, particularly when oxide CMP is inserted toplanarize the ILD layer. As such, pattern-dependent oxide deposition andoxide CMP models may be used and may require the loading of modelcalibration parameters 520. The use of the prediction component in thismanner may also facilitate the introduction of low-k materials into adamascene process flow. The result of this step is a prediction of thefinal ILD thickness 546.

[0189] Depending on whether the prediction is part of mode A (FIG. 11)or mode B (FIG. 12) the flow has an option 548. In mode A 552, anyfeature dimension variation outside of the specification for level 1 hasbeen used to modify the design such that the printed feature dimensionfor level 1 matches that of the design. So for mode A, the ILD thickness546 can be fed directly into the etch model 566 on FIG. 21C.

[0190] In mode B 550, the feature size variation that results from thelithography step needs to be used to update the layout extraction to theproper feature variation that downstream processes will receive. In thismode, the incoming wafer topography and layout parameters are loadedinto the lithography model 554. It is recommended to includepattern-dependencies in the lithography model to acquire full-chipprediction and as such, model calibration parameters may be required andloaded 520. The feature size variation 556 is predicted and used toadjust layout features, shrink or bloat features, to accuratelyrepresent the result of lithography 558. The layout is generated 560 andused to generate a new extraction 562 that more accurately representsthe effects of litho-based feature dimension variation. The newextraction 564 is fed forward to the etch process step 566. For anN-level interconnect process flow prediction in model B, this step willbe repeated for each lithography step so that the full impact of featuredimension variation may be observed at level N.

[0191] The ILD thickness from the prior step 566 and the layoutparameters are loaded into an etch model. It is recommended to includepattern-dependencies in the etch model to acquire fill-chip predictionand as such, model calibration parameters may be required and loaded520. The etch model predicts final wafer topography 568, which, alongwith the layout parameters, is loaded into an ECD model 570. It isrecommended to include pattern-dependencies in the ECD model 570 toacquire full-chip prediction and as such, model calibration parametersmay be required and loaded 520. The result of this step is a full-chipprediction of wafer topography after plating 572. Some processes mayalso use an electrical chemical mechanical deposition (ECMD) stepinstead and the use of pattern dependent models is recommended.

[0192] The incoming wafer topography resulting from ECD and extractionparameters are loaded into the CMP process model or models 574. CMP in adamascene process may be performed over a number of process steps. Atypical example is when a bulk CMP step is used to remove most of thecopper, a touchdown or endpoint polish is then done to slowly clear allthe copper from the field areas without significant dishing and erosionof features and finally a barrier polish is performed to remove thebarrier material. It is recommended to include pattern-dependencies inthe CMP model to acquire full-chip prediction and as such, modelcalibration parameters may be required and loaded 520. The final wafertopography that results from the CMP step or flow is generated 575. Someof the wafer topography characteristics may include thickness, surfaceprofile, dishing and erosion.

[0193] An optional step may be to include electrical extraction orperformance analysis for the current, completed interconnects level 576.Electrical characteristics that may be predicted from the full-chip CMPprediction include sheet resistance, capacitance, drive current, and,when multiple interconnect levels are considered, timing closureanalysis. This step may be useful when verification is done to analyzethe impact of lithography-based feature dimension variation on ICperformance. Often feature dimension tolerances or specifications maynot provide the level of resolution necessary to properly gauge theimpact of feature dimension variation and this might be one way to gaina better characterization.

[0194] While the CMP step is the last physical process step in the priorinterconnect level (e.g. level 1), the ILD deposition for the currentinterconnect level (e.g. level 2) needs to be predicted to acquire thewafer surface topography used in lithography prediction for the currentinterconnect level (e.g. level 2). Wafer topography and extractedparameters are loaded into the ILD process model to predict theresulting wafer surface or thickness 580. ILD deposition models mayinclude the use of oxide (SiO₂) or low-k material. It is recommended toinclude pattern-dependencies to acquire full-chip prediction,particularly when oxide CMP is inserted to planarize the ILD layer. Assuch, pattern-dependent oxide deposition and oxide CMP models may beused and may require the loading of model calibration parameters 520.The use of the prediction component in this manner may also facilitatethe introduction of low-k materials into a damascene process flow. Theresult of this step is a prediction of the wafer surface beforephotoresist is added and lithography is performed 580. The wafer surfacetopography is saved in a database or filesystem for use in prediction insubsequent interconnect levels 578. Although it is not necessary to feedwafer topography forward between interconnect levels, it is recommended,particularly in cases where an oxide CMP step is not performed after ILDdeposition.

[0195] Although photoresist deposition is not explicitly shown in thisflow, in cases where pattern dependencies affect planarity ofphotoresist, then pattern-dependent photoresist models may beincorporated between ILD deposition and lithography models (orincorporated directly into the lithography models using test-wafers andlumping the photoresist and lithography effects into one model).

[0196] d. Prediction of Feature Dimension Variation Using LithographyModels

[0197] The lithography modeling and prediction component could beconsidered part of the process modeling component. However the processmodeling component 400 inputs pre-process wafer topography and predictspost-process wafer topography at each step in the flow. Where as thelithography component inputs incoming wafer topography, along with thedesign or pattern to be imaged, and predicts feature dimensionvariation. As such they are treated as separate components (section c.and section d.) in this description.

[0198] As illustrated in FIG. 22A, the predicted wafer topographyvariation (Δh) across the chip 580 (e.g., the topography resulting fromprocessing levels 1 through N) and the current layout information 601,design or extraction, (e.g., the design from level N+1) are input intolithography modeling component 600 which is used to map the predictedwafer topography and desired (or designed) feature width (FW*) to thelithography printed feature dimension (for example, feature width(FW_(p))) variation across the chip 740. The lithography process flow600 may also characterize pattern dependencies 640 in lithography due tosub-wavelength distortions using data from test wafers or opticalmathematical relationships. This mapping may be computed within thesystem or the results from optical proximity correction (OPC) may becomputed, loaded into the system and used. The result is that predictedvariation in printed feature dimension would address width variation dueto topography and distortion, shown respectively in FIGS. 8 and 9.

[0199] To capture pattern dependent width variation due to etchprocessing or to map topographical variation to etched features, an etchmodel may be used 641 to map printed features to the physically etchedfeatures. As shown in FIG. 22B, component 641 acquires 651 the printedfeature variation that results from topographical 620 and distortion640. An etch model is used to characterize pattern dependencies and mapfull-chip printed feature variation to physical or etched featurevariation. The etch model prediction may also include etchcharacteristics such as trench depth, sidewall angle and trench width. Atable is constructed that maps 655 printed variation from each discretegrid from layout extraction to physical feature variation. The variationmay also be applied 656 to the layout features within each grid toadjust the full-chip design to the printed and physical variation,depending on whether the prediction resolution needs to be at the gridor discrete feature level. (When 600 is used in conjunction withverification component 810, the grid level feature variation is appliedto the discrete layout features and step 656 may be skipped). By usingcomponents 620, 640 and 641 within the lithography flow model 600, theprimary contributors to feature width variation may be characterized andpredicted 740. The optional etch component 641 may be used with eitherof the two approaches described in the following paragraphs.

[0200] A graphical illustration that depicts the current layoutinformation projected onto the predicted surface topography for a die608 is shown in FIG. 23. The die is discretized to the level chosen instep 312 of layout extraction, which controls the resolution of thethickness and feature dimension variation prediction. The lithographymodeling component 600 maps 612 the designed width and die surfaceheight at that grid location to corresponding feature variation (forexample, in FW or CD) at the same grid location 364. The mapping doesthis for all grid locations across the die, resulting in a full die mapof feature dimension variation.

[0201] Two ways for computing feature dimension variation from chiptopography are described. The first approach, shown in FIGS. 24 and 25,uses conventional optical proximity correction type tools to determinethe effects of feature density and optical interference during theactual imaging. The second approach, shown in FIGS. 26 and 27, uses testwafers and calibration methods to characterize both topographical andpattern interference effects due to sub-wavelength distortion.

[0202]FIG. 24 describes the steps for mapping chip surface height ortopography variation and current design features to variation in thelithography printed or imaged feature dimensions of those features 620.The predicted full-chip topography (Δh), consisting of each discreteelement across the die, is loaded 622 into the component 620 along withthe current design or extraction 601. The difference between chiptopography and a common reference, for example a test or alignment keynear the edge of the die, is computed 624. Since the imaging systemfocal length may be adjusted to an alignment or test key, this wouldallow for rapid computation of features within and outside thedepth-of-focus. A table is assembled that maps chip-level heightvariation to layout features (e.g., metal level N+1) within eachdiscrete grid. There are a number of optical mathematical expressionsfor relating focal distance to feature resolution that may be used tomap 626 chip surface topography and design features. Similarly, thereare tools for mapping layout extraction parameters to the associatedfeature dimension variation for each feature, grid, or an aggregatemetric (e.g. maximum or mean) for the entire die. A common relationshipmay be derived from the well-known Rayleigh equations for optics, usingk₁ and k₂ constants appropriately derived or provided for a particularlithography tool. The variation in feature dimension can be applied tothe layout features within the grid resolution of the chip surfaceprediction to generate a full-chip prediction of printed featuredimension (e.g. FW or CD) 628. The full-chip prediction of printedfeature dimensions (e.g. line widths) is provided 740 to theverification component 800.

[0203]FIG. 25 describes the steps for mapping pattern feature densitiesto variation in lithography printed or imaged feature dimensions 640.The layout for the current design level is loaded and a table isassembled that maps layout features to discrete grids in chip surfacetopography prediction Δh. Conventional optical proximity algorithms,many of which are commercially available in EDA tools, are used to mapfeature density to feature dimension variation 644. The computed featuredimension variation is at the layout feature resolution that is providedat both the layout resolution and extraction resolution 646. Theresulting computation of feature dimension or feature width variation isthen provided 740 to the verification component 800.

[0204] The second approach to implementing the lithography modeling andprediction component 600 is illustrated in FIGS. 26 and 27. The secondapproach uses methods described in section c. to generate a calibratedlithography model for relating surface height, designed CD, and featurewidth FW, and pattern interference effects to feature dimensionvariation (e.g. CD and FW). The model is calibrated using the stepsdescribed in section c. and illustrated in the flow diagram of FIG. 20.

[0205] The use of test wafers for calibrating a lithography model for aparticular tool and settings are illustrated in FIG. 26A. A lithographytest wafer die 679 is patterned with a range of width and space values680 (density can be computed given both FW and FS) that may include oneor more levels of structures. The structures on these levels may bechosen to represent multi-layer effects of variations in line widths andlines, and via chains and other structures, to capture patterndependencies associated with design levels of interest (e.g.interconnect levels). Further details and examples of test waferstructures that may be used are provided in section f. The test wafer isprocessed on a lithography tool using a given recipe 681 and then asubsequent etch process is performed to remove material according tocritical dimensions printed during lithography. The resulting variationin feature dimensions (e.g. CD or FW) is measured across the chip 683using a metrology tool 684 (e.g., an SEM, a physical surface profilingtool, or an optical feature profiling tool). The measured parameters areused to calibrate a lithography model that provides the mapping 682between the two spaces 680 and 684. This mapping, dictated by thecalibration model parameters, may be considered a model that maps a widerange of feature and surface topography values 680 to a particularfeature size variation 684 for this tool and recipe.

[0206] These mappings or calibrated models may be used for predictingfeature size variation for new IC designs, as shown in FIG. 26B. Thewidth, space (and density) of features that fall within the range 686spanned by the test die are extracted 685 from a new IC layout. Theextracted features 685 for spatial locations across the chip 486 areinput into the mapping 682 and an accurate prediction of feature sizevariation across the chip 689 and 690 can be acquired for a given tooland a given recipe before processing of the new IC design.

[0207] The predicted process variation may also be fed into electricalmodels or simulations to assess the impact of processing on theelectrical performance of the chip, similarly to what is shown in FIG.19B. Some of the electrical parameters that may be computed Using themodels include variation in sheet resistance, resistance, capacitance,interconnect RC delay, voltage drop, drive current loss, dielectricconstant, timing closure, signal integrity, IR drop or cross-talk noise.These predictions can be used to determine the impact of feature sizevariation on electrical performance.

[0208]FIG. 27 describes the steps for computing predicted featuredimension variation using pattern-dependent lithography models. Thisapproach may also use lithography test wafers, examples of which areprovided in section f., to calibrate the model to, for example, aparticular lithography tool, features, or a stack of levels below thecurrent design level, and photoresist type. The predicted (or in somecases, measured) chip level surface height variation Δh from the priorprocess step or steps (e.g. ILD deposition, oxide CMP, or photoresistspin-on) is loaded 580. The layout information associated with thecurrent design level, which may consist of layouts, extractions, or acombination of them, is also loaded from file system or database 601.The calibration model parameters are loaded into the model forprediction 602. A pattern-dependent lithography model is used to predictfeature size variation for the given design layout 674 and provides 740it to the verification component 800.

[0209] e. Verification and Correction of Lithographic Feature DimensionVariation

[0210] The predicted feature dimensions are then compared to the designspecifications to verify that none of the printed (or etched) featureswould exceed the specifications and tolerances for the design. Thosesites or features that do exceed the tolerances are identified and theircoordinates stored. As described in FIG. 10, the feature widthvariations may also be used to modify a design file, which can be fedinto an electrical simulation to examine the electrical impact onperformance. The feature width variation may also be combined withtopographical variation for full interconnect level electricalcharacterization as well. Within the context of the methods describedhere, electrical impact also includes full-chip prediction of sheetresistance, total copper loss, capacitance, drive current and timingclosure parameters. In the verification mode, modification to the designfile of the feature width variation is primarily for simulation purposesand to simply reflect the variation induced by manufacturing. Suchdesign files would not be used for mask creation. To correct for thepredicted feature width variation, the following mask correctionapproach may be performed.

[0211] The user may also choose to have the system correct the designedfeatures used in making the masks so that the actual printed dimensionswould equal the desired or designed values. The corrected design is thenused during tape-out to construct masks such that the actual lithographyprinted dimensions and features yield those originally designed anddesired. The following paragraphs and figures describe the verificationand correction components.

[0212] A flow diagram of how the verification and correction componentfits into the overall concept is shown in FIG. 28. Layout information,which may include design and extraction data 601, predicted criticaldimensions, and feature sizes 680, are loaded into the verification andcorrection component 800. The critical dimension and feature sizespecifications are also loaded 750 and, optionally, electricalspecifications may be loaded for comparison with simulated electricalperformance of the printed circuit dimensions. Verification performs acomparison between predicted and specified dimensions and identifiesthose features that exceed design tolerances (e.g., feature sizevariation or electrical performance). The verification component may beused alone or in conjunction with the correction component 830 to modifythe layout (e.g., GDS file) to produce the desired printed circuitdimensions. Depending on whether either or both verification andcorrection components are used, the results may be saved to a filesystem or database for further viewing and analysis by the user 930.When correction 830 is used, the resulting layout may be further testedfor sub-wavelength optical distortion and optical proximity correctionor directly sent in the form of a GDS file to the mask tape-out process,the first step of mask creation 930.

[0213] The verification component may be implemented in three waysdepending upon how the user has specified the grid resolution of layoutextraction 312, which also defines the resolution of the topographyprediction. As described in section a., a finer grid resolution duringextraction generally provides a more accurate representation of theminimum feature sizes on the chip. However there is a significantincrease in the computational time and resources necessary to shrinkgrid size to finer features. It is left to the user to determine thecorrect tradeoff; however the following paragraphs provide twoapproaches to verification that address grid resolution larger (shown inFIG. 29A) than the feature dimensions and smaller (shown in FIG. 29B)than the feature dimensions. It is unlikely that one could choose asingle grid resolution that would accommodate all IC features. Howeverin the case that hierarchical grid resolution is tailored to underlyingfeature size, a method is also shown in FIG. 29C for verification whenthe grid resolution matches the feature resolution or it iscomputationally necessary to use the grid resolution.

[0214] In all cases, feature width variation may be imported intoelectrical simulation or extraction tools to characterize the electricalimpact as well as the physical impact. It may also be beneficial toverify the electrical performance of a complete interconnect level andas such, one may combine topographical variation from subsequent ECD orCMP steps and import both variation calculations into electricalextraction tools. Such electrical characterization could be performed atthe full-chip level or for some critical sub-portion of the circuit.

[0215] Another approach is described in FIG. 29D that uses a statisticaldescription of each grid (e.g. maximum, minimum, and mean feature size,or density) to determine if any features on the chip will violatetolerances. While computationally much faster, this approach may provideless accuracy than the approaches in FIGS. 29A, 29B and 29C in terms ofmodifying the individual features within the discrete grids. In thisapproach, a general heuristic is used to change features relative thedistribution for that grid (e.g., shrink the minimum features within agrid by 10%).

[0216] Verification for discrete grid sizes greater than the minimum ICdimensions is described in FIG. 29A. In the first step, the designlayout for the current layout level (e.g., interconnect level N+1) andthe lithography step are loaded 812. The full-chip predicted featuredimension variation 680 from lithography is also loaded 814. Thepredicted variation for each grid is apportioned to the features withinthe grid according to the (possibly probabilistic) distribution offeature dimensions within the grid 816. For interconnect levels, much ofthis apportionment may be the shrinking and bloating of lines. This step816 is done to provide a common basis for comparison between the layoutfeature and predicted dimensions. The design specifications andtolerances for the chip or given IC level are loaded into the system818. A comparison is made between the mapped variation from step 816 andthe specifications 820 and those values that exceed the given toleranceare stored 822. The user is then notified whether the current design hasany areas that exceed the tolerance and, if not, the design is certifiedas passing the verification check.

[0217] Verification for discrete grid sizes less than the minimum ICdimensions is described for Option A in FIG. 29B. The only differencebetween FIGS. 29A and 29B is the third step 826 where, in FIG. 29B, thevalues for discrete grids are averaged over a feature dimension tocompute a predicted value at the same resolution as the layout. This isdone to provide a common basis for comparison between the layout featureand predicted dimensions.

[0218] Verification for discrete grid sizes that are equal to theminimum IC dimensions is described for Option C in FIG. 29C. The onlydifference between FIG. 29C and FIGS. 29A and 29B is the removal of anyneed to transform the predicted values to the same resolution as thelayout and as such, there is no need for any step 816 or step 826.Additionally this approach can be used with a general heuristic thatchecks for violations at the extraction resolution, computes corrections(in 830 of FIG. 30) and applies them to all features within the grid(e.g., shrink all widths within the grid by 10%).

[0219] Another option, Option D, which is described in FIG. 29D, iscomputationally simpler than the other described methods but may providea less accurate assessment of feature dimensions. Rather than transformthe grid resolution to the layout resolution, the minimum, maximum, andmean widths or feature sizes are used to generate a distribution ofpredicted feature variation for each grid 828. The feature size designspecifications and tolerances are compared 829 with the distribution offeature dimension variation computed in 828 and the corrections (in 830of FIG. 30) are applied using a heuristic (e.g., bloat the minimum linewidths by 10%). Otherwise, the steps for Options C and D are verysimilar.

[0220] Verification results may be provided to the correction component830, as illustrated in FIG. 30. In this component, modifications arecomputed for individual feature dimensions that exceed the designtolerances 832 and are used to physically modify feature dimensions inthe electronic design layout to produce the desired printed or etchedfeature dimensions 920. In certain cases, dummy fill or other geometriesmay need to be repositioned. The design layout is then re-generated 280and if dummy fill is modified significantly, a new extraction performed.

[0221] Two approaches for computing modifications to the layout aredescribed in FIGS. 31 and 32. In the following descriptions, featuredimensions related to feature width (FW), feature space (FS) andcritical dimension (CD) are used as an example of how a featuredimension is adjusted or computed but another feature dimension may beconsidered as well. The first approach, shown in FIG. 31, uses theinverse, pseudo-inverse, or partial derivatives of the M_(L) component600 to map errors in printed feature width FW_(p) to the desired widthFW* in the layout. This approach begins with the first grid location orfeature that exceeds tolerance 834. The desired FW*, FS* or othercritical dimensions 601 may be acquired from the extraction table ordirectly from the current layout level 836. Either the predictedlithography-based printed dimensions FW_(p) from the M_(L) prediction,or the feature-level predicted variation computed in steps 816 or 826 isacquired 838 from the verification component. The surface topography his also acquired from the M_(p) prediction 840 for use in the mapping ofthe desired and printed line width spaces. The computations described inFIG. 33B are used to compute the partial derivative or gradient$\frac{\partial{FW}^{*}}{\partial{FW}_{p}}$

[0222] for the given topography h. Another approach is to invert theM_(L) transformation 600 described in FIGS. 22, 24 and 25, to yield:

FW*=f(FW _(p))|_(h)

[0223] where f is the explicit or approximate inverse of M_(L). TheM_(L) transformation 600 may be optical equations (e.g. derived fromRayleigh relationships) applied to a particular lithography tool or apattern-dependent model developed using a lithography test wafer. Theerror between the desired and printed dimension is computed 844 as:E=f(FW*−FW_(p)). An adjustment to the feature is computed as:${\Delta \quad W} = {E \cdot \frac{\partial{FW}^{*}}{\partial{FW}_{p}}}$

[0224] where ΔW is the adjustment to a feature width or dimension 846and may be done using the procedure illustrated in 33B. In aninterconnect level, ΔW may be a shrinking or bloating of an array oflines. The predicted FW_(p) variation is recomputed for the modifiedwidth 848 and the system iterates on steps 844, 846 and 848 until theerror is within design tolerance. A check is made to see if all grids orfeatures that exceed tolerance have been adjusted, and if not theprocess continues 852. If so 851, then the layout is physically modified920.

[0225] The second approach, shown in FIG. 32, uses data obtained using alithography test wafer to map errors in printed feature width FW_(p) tothe desired feature width FW* in the layout. This approach begins withthe first grid location or feature that exceeds tolerance 853. Thedesired FW*, FS*, or other feature dimensions 601 may be acquired fromthe extraction table or directly from the current layout level 854.Either the predicted lithography-based printed dimensions FW_(p) fromthe M_(L) prediction or the feature-level predicted variation computedin steps 816 or 826 are acquired from the verification component 855.The surface topography h is also acquired from the M_(p) prediction 840for use in the mapping of the desired and printed line width spaces. Thecomputations, also described in FIGS. 33B and 34C, may be used tocompute the partial derivative or gradient$\frac{\partial{FW}^{*}}{\partial{FW}_{p}}$

[0226] for the given topography h. Another approach is to invert theM_(L) transformation 600 developed using the calibrated model to yield:

FW*=f(FW _(p))|_(h)

[0227] where f is the explicit or approximate inverse of M_(L). Theerror between the desired and printed dimension or line width iscomputed 858 as: E=f(FW*−FW_(p)). An adjustment to the feature iscomputed as:${\Delta \quad W} = {E \cdot \frac{\partial{FW}^{*}}{\partial{FW}_{p}}}$

[0228] where ΔW is the adjustment to a feature width or dimension 860.In an interconnect level, ΔW may be a shrinking or bloating of an arrayof lines. The predicted FW_(p) variation is recomputed for the modifiedfeature width 862 and the system iterates 865 on steps 858, 860, 862 and864 until the error is within design tolerance. A check is made to seeif all grids or features that exceed tolerance have been adjusted, andif not the process continues 868. If so 867, then the layout isphysically modified 920.

[0229] The feed-forward mapping from desired feature widths ordimensions FW* to printed feature widths or dimensions LW_(p) is shownin FIG. 33A. The process models 873 predict chip surface topography h874, which is then fed into the lithography model M_(L) 875 along withthe desired dimensions 872 from the design FW*, FS*, or CD*. Thelithography model 875 maps the desired width and associated chiptopography to the actual printed FW_(p) that occurs as a result of thelithography process 876. This mapping can be used to mathematicallyrelate desired circuit dimensions to lithography printed dimensions fora given chip topography.

[0230] When such a mapping is not mathematically invertible or may becomplex and nonlinear, a partial derivative can be used to provide alinear approximation of the inverse close to the feature dimensions ofinterest. This mechanism for relating variation in printed dimensionsback to the desired dimensions is illustrated in FIG. 33B. The error,which may be some function of the variation between desired and printeddimensions, is computed 880. The predicted chip topography h is alsoused 881. There are several ways to compute the gradient or partialderivative of the desired dimensions with respect to the printeddimensions. One approach may be to use data from a processed andmeasured lithography test wafer, described in FIG. 34C and described ingreater detail in section f. Another approach may be to feed featurewidth values near the desired FW* into the M_(L) component and store theresulting printed width variation FW_(p). From this table of values, thepartial derivatives can be computed as the change in FW* with respect toFW_(p) using procedures found in many calculus and applied mathematicstextbooks. Another approach, which may be applicable if M_(L) includes aseries of equations, is to linearize the equations about the line widthor feature size of interest. Linearization methods are provided in mostmajor applied mathematics and multi-variable controls textbooks.

[0231] The verification and correction components are the final steps incomputing the electronic design to be used in mask creation for eachdesign level (e.g., interconnect level). A summary is shown in FIG. 35,illustrating how the components described in sections a. through e. arecombined and used in an iterative fashion on each subsequent designlevel. For the first interconnect level 1001, the layout 1010 is usedwith a prediction component 1012 to generate chip-level topography whichis used along with the feature dimensions at the current design level toverify and correct any variation 1014 to the desired feature sizetolerances 1016. This process is repeated 1018 until all printed oretched feature dimensions, design, and electrical parameters (for thatlevel) are within design and feature size tolerances.

[0232] The full-chip topography for interconnect level 1 is propagatedto level 2 1020. For the second interconnect level 1002, the layout 1022is used with a prediction component 1024 to generate chip-leveltopography which is used along with the critical dimensions at thecurrent design level (in this case, level 2) to verify and correct anyvariation 1026 to the desired feature size tolerances 1028. This processis repeated 1030 until all printed or etched dimensions, design, andelectrical parameters are within tolerance. The full-chip topography forinterconnect level 2 is then propagated to level 3 1032 and the processcontinues until the final interconnect level is reached.

[0233] f. Creation and Use of Lithography Test Wafers

[0234] As described in the calibration procedures in section b., testwafers use a variety of test structures to map the relationship betweencircuit features and pattern dependencies within one or more processsteps. The methods we describe include the creation and use of testwafers to capture pattern dependencies for lithography tools,photoresist materials, and deposition or a subsequent etch. Alithography test wafer may include test structures that characterizefeature density and incoming topography (both single and multi-leveleffects) with regard to the printed critical dimensions. The test wafersimulates the variety of topography that an incoming wafer with apatterned circuit may have and does so by creating a controlledexperiment where structures are varied to span a space of potentialcircuit patterns.

[0235]FIG. 36A illustrates how a test wafer may be used to characterizepattern dependencies in a lithography process. The pre-processed testwafer topography is measured according to a measurement recipe thatincludes x and y site locations 1600. (Additional information concerningmeasurement recipes may be found in U.S. patent application Ser. No.10/200,660, filed Jul. 22, 2002.) The measured data is assembled in atable that relates underlying circuit patterns (e.g. feature widths FW*and feature spaces FS) and the surface topography h (e.g. thickness)1602 for each x and y site location. The wafer is processed using theactual lithography process flow that is to be used with the finalproduction ICs. The lithography process flow may include multiple stepssuch as photoresist deposition, lithographic imaging, and a subsequentetch step. After processing the resulting width variation, in the formof printed or etched feature dimensions (e.g., widths FW_(p) andspaces), are measured 1606 and calculated 1608 at the x and y sitelocations.

[0236] A table of results are generated 1610 that may be used forcalibrating a pattern dependent lithography model, correcting designfeatures to yield desired printed or etched dimensions, or evaluatingbest practices (e.g., tool and process recipes) and consumables (e.g.,photoresist materials) for a particular process flow, lithography andetch tool. An example of such a table is shown in FIG. 36B, where the(x, y) site locations are stored in columns 1620 and 1622, the designedor desired line widths for (x, y) in column 1624, the measured surfacetopography for (x, y) in column 1626, the printed or etched dimensionsfor (x, y) in column 1628 and the difference between desired and printed(etched) features in column 1630.

[0237] “Printed” and “etched” are terms often used interchangeably inthis description. The reason is that it is often difficult to measurethe printed line width right after lithography imaging, so an etch stepis performed so that the features may be easier measured. Also etch maycontribute to the overall width variation, as well as variation in thetrench depth and sidewall, as a result of pattern dependencies. Asstated throughout this description it may be beneficial when predictingtotal feature width or size variation to consider lithography and etchtogether (as a flow) to address both printed and etched variation. Theimprovement of within-die etch uniformity and the availability ofcertain sensors and measurement approaches may eliminate the need toperform the etch step and provide direct measurements of printedfeatures. This approach and these wafers may be used in both cases.

[0238] A test wafer to capture pattern dependencies in lithographyprocesses is shown in the following figures. FIG. 37A shows amulti-level test wafer stack that begins with a silicon wafer 1056,followed by an ILD layer (e.g. oxide or low-k) 1054, a metal 1 layer1052, a via 1 level 1051, and a metal 2 layer 1050. The test wafer stackis used to relate topographical variation with regard to underlyingpatterns.

[0239] An example of a layout for metal level 1 is illustrated in FIG.37B. A section of varying line widths and spaces is used in metal level1 1100 to capture width and space dependencies in interconnect levels. Asection of varying array sizes are used in metal level 1 1200 to capturepattern interactions between arrays and vias. A section of varyingslotting structures are used in metal level 1 1250 to capturemulti-layer pattern interactions between slotting structures, lines, andvias.

[0240] An example of a layout for via level 1 is illustrated in FIG.37C. A section of fixed size and space via arrays are used 1400 tocapture pattern interactions between via arrays and varying arraystructures in metal level 1. A section of fixed size and space viachains are used 1500 to capture pattern interactions between via chainsand varying slotting structures in metal level 1. The via level areabetween varying line widths and spaces region is an ILD section with nostructures to capture interactions between lines in metal levels 1299.

[0241] An example of a layout for metal level 2 is illustrated in FIG.37D. A section of overlap line width and space structures are used inmetal level 2 1300 to capture width and space dependencies betweeninterconnect levels. Another section of overlap width and spacestructures are used in metal level 2 1401 to capture dependenciesbetween via arrays and metal levels. Another section of overlap widthand space structures are used in metal level 2 1501 to captureinterlayer dependencies among via lines, arrays, and slottingstructures.

[0242] The next few paragraphs and figures will describe the line widthand space interaction sections across the metal 1, via 1, and metal 2layers with structures in areas 1100, 1299 and 1300 respectively. FIG.38 illustrates varying line widths and spaces 1110 across the largercomponent 1100 for metal level 1. FIG. 39 illustrates one arraystructure 1120 (within the 1100 section) with a fixed width of 0.35micron 1123 and space of 0.35 micron 1121 within each sub-section (suchas 1120) in metal level 1.

[0243] The via level between section 1100 of metal level 1 and section1300 of metal level 2 is a solid ILD field (e.g. oxide or low-kmaterial), so there are no structures. FIG. 40 illustrates the type ofstructures in metal level 2 in section 1122 of larger area 1300. Thegoal is to characterize line width and line space interactions betweenmetal levels, so section 1300 has varying widths and spaces that overlapwith the fixed width and space in metal level 1 component 1120. Thisoverlap allows for combinations of width and space values to better spanthe space of all potential width and space combinations used in aproduction circuit. In this example, there are four overlap structures(1128, 1129, 1130, 1131) within component 1122, which also lies withinthe larger section 1300. One area has a line width of 0.25 micron andline space of 0.25 micron 1128. Another area has a line width of 2microns and line space of zero microns 1129. Another area has a linewidth of 0.13 micron and line space of 0.13 micron 1131. Another areahas a line width of 0.50 micron and line space of 0.50 micron 1130.

[0244]FIGS. 41A and 41B illustrate the overlap of the two metal levels.FIG. 41A shows the structure 1124 with a fixed line width and line spacein the metal 1 level. FIG. 41A also shows the structure 1126 withvarying line widths and spaces in the metal 2 level. FIG. 41Billustrates how the test wafer characterizes the interaction of the twolevels by superimposing metal 2 on the metal 1 component. The overlapstructures are indicated in 1140, 1142, 1144, and 1146. The via level 1for area 1299 is a large ILD section which electrically separates thetwo metal levels and thus is not shown here.

[0245] The next set of figures and paragraphs describe the sections ofstructures that characterize array and via interaction 1200. FIG. 42illustrates a sample layout of structures in section 1200 of metallevel 1. The area defined in 1212 is magnified to show the type of largearray structures 1211 within an oxide field 1210. FIG. 43 shows, for thearea 1415 in via level 1 above 1212 in metal level 1, the type of largearrays of vias 1412, shown as gray squares in the magnified section1410.

[0246]FIGS. 44A and 44B illustrate the overlap of the metal and vialevels. FIG. 44A shows the large array structures 1210 in the metal 1level. FIG. 44A also shows via structures in the via 1 level. FIG. 44Billustrates how the test wafer characterizes the interaction of the twolevels by superimposing via level 1 on the metal 1 component. Theoverlap structures are indicated as 1211 and 1412.

[0247] The next set of figures and paragraphs describe the structuresthat characterize the interaction between slotting structures, viachains, and overlapping metal lines. FIG. 45A shows the slottingstructure area 1250 of metal level 1 with three areas 1540, 1542, and1544 selected for depicting examples in FIG. 45B. In FIG. 45B, anexample of lines with no slotting material are shown 1540. Examples oftwo different slotting types are shown in 1542 and 1544. A legend forthe metal 1 (M1), via 1, and metal 2 (M2) levels for this section isprovided 1546. FIG. 45C superimposes via chain structures of via level 1(shown in 1550, 1552 and 1554) over the slotting structures 1540, 1542and 1544 shown in FIG. 45B. FIG. 45D superimposes the metal 2 overlaplines that connect to metal level 1 through the via structures for thethree types 1560, 1562, and 1564 of slotting structures. A legend isprovided in 1566. This completes the description of the three areas ofstructures in this particular layout example.

[0248] The lithography test wafer concept illustrated in the priorfigures is not limited to these structures and may include any number ofstructures that can be used to characterize interaction of featurewidth, feature spacing, dummy fill, or slotting structures between metallevels and other via and metal levels. While it is not necessary to usethe actual process flow preceding the lithography process step to becharacterized, it is recommended when it is important to capture thetypes of incoming process dependent pattern dependencies the lithographyprocess will receive. Actual processing in creating the test wafer mayalso be useful in characterizing the CMP and ECD processes that precedelithography as well.

[0249] g. Applications

[0250] There is a wide range of applications for the methods describedabove. Two ways in which chip-level pattern dependencies, topographicalvariation, and imaged pattern densities respectively, cause variation inlithographic feature dimensions are shown in FIGS. 8 and 9. Thefollowing figures and paragraphs describe solutions using the proceduresdescribed in sections a. through f.

[0251] The next two figures describe solutions for the problems outlinedin FIGS. 8 and 9. FIG. 46A describes how the methods may be applied toaddress the first problem of chip-level topographical variation. FIG.46B illustrates the surface topography variation from FIG. 8 with thesolution described in FIG. 46A. In this application, the level N layout2010 is loaded into a computer where the methods described above havebeen implemented in software 2008. The process model predictioncomponent 2012 performs required extractions and predicts the chip-levelsurface topography 2014. This variation in topography is also shown inFIG. 46B 2046, as well as the height variation at each grid location2048. The incoming chip-level topography 2014 and the level N+1 layout2026 are loaded into the lithography model component 2016, which is usedto predict the feature size (e.g. line width) variation 2018. Patterndependencies may also be extracted from level N+1 layout and used aswell 2013. The design tolerances 2022 are loaded into the computer 2008and compared 2020 to the predicted dimensions. The verification andcorrection component 2024 adjusts the layout and the process iteratesuntil satisfactory printed feature sizes (e.g. line widths) areachieved. The layout is then used to create the mask for layout levelN+1. The results of the solution described in FIG. 46A are shown in FIG.46B where the level N+1 mask 2039 feature dimensions w_(a) 2042 andw_(b) 2044 are adjusted 2040 in the layout such that the printedfeatures w₂ 2050 and w₁ 2052 are the desired width. This solution allowsthe lithography process to adjust printed features to within-die filmthickness variation 2048.

[0252]FIG. 47A describes an application to address the second problem offeature density variations that were described in FIG. 9. FIG. 47Billustrates a variation in feature densities, similar to that shown inFIG. 9, with the methods applied in FIG. 47A. In this application, thelevel N layout 2070 is loaded into a computer where the methods havebeen implemented in software 2069. The process model predictioncomponent 2072 performs required extractions and predicts the chip-levelsurface topography that may or may not be used in conjunction withfeature density information. Since optical interference due to featuredensity may vary with depth of focus, topographical information may beuseful.

[0253] The level N+1 layout 2071 is loaded into an extraction tool 2075,which extracts pattern density information. The extraction may beperformed using the procedure described in section b. of an EDA tool orby using an optical proximity correction tool. The feature densityextraction and topographical information 2074 are loaded into alithography model component 2076, which is used to predict the featuresize variation 2078. The design tolerances 2082 are loaded into thecomputer 2069 and compared 2080 to the predicted dimensions. Theverification and correction component 2084 adjusts the layout and theprocess iterates until acceptable printed feature sizes are acquired.The layout is then used to create the mask for layout level N+1. Theresults of the solution described in FIG. 47A are shown in FIG. 47Bwhere the level N+1 mask 2092 feature dimensions w_(a) 2096 and w_(b)2098 are adjusted 2084 in the layout such that the printed features w+Δ12102 and w+Δ2₂₁₀₄ are the desired width. This solution allows thelithography process to adjust printed features to variation in featuredensities, whether the film thickness is planar 2100 or varying 2046 (asshown in FIG. 46).

[0254] The method may also provide functionality similar to conventionalstepper technology. Whereas stepper technology allows lithographicimaging to adapt to wafer-level non-uniformity (such as bow or warp),the techniques may be used to adjust lithographic imaging to chip-levelor within-die variation. A basic illustration of how stepper technologyworks is illustrated in FIG. 48, which shows a mask with an IC pattern2220 to be imaged onto the wafer surface at points A 2208 and B 2209 atdifferent heights. Steppers normally print within a defined area orfield that may include one or more die. The lithography tool measuresthe alignment marks 2212 and 2214 for both x and y alignments and tilt.Wafer-level variation 2210 such as warping and bowing is common wherethe characteristics of wafer surface at point A 2208 may be differentthan the wafer surface at point B 2209. The tool adapts the mask orreticle 2220 and associated optics to compensate for this variation overlonger distances. The focal plane f 2218 may or may not be adjusted tomaximize the resolving power. There also exist step and scan tools thatexpose the die in strips where the pattern is stitched together on eachstrip. In most of these applications, steppers adjust to wafertopography on length scales of 1 to 50 mm. Within-die or chip-leveltopography may vary at similar magnitudes as wafer-level; however theselength scales are on the order of 0.00008 mm to 25 mm. This situation isillustrated in the case shown in FIG. 49 where the mask or reticle 2223is adjusted (to wafer surface A 2208 of FIG. 48) to print IC featuresonto an ILD layer of a wafer 2201. The adjustments are made with regardto x and y alignment marks 2222 and tilt and potentially, focal distancef 2221. However chip-level variation 2224 occurs on a much smallerlength scale and certain features that are sufficiently different thanthe focal length may likely exceed the critical dimension tolerancesspecified in the design specification 2228.

[0255] The methods we have described may be used to complementconventional wafer-level stepper technology and work as a miniaturestepper that adjusts to chip-level variation in printed images. Themethods may be applied as a chip-level lithography correction stepper(CLiCS) system 2266 that receives the following inputs: layout anddesign specifications 2260, lithography tool parameters and settings2262 and test wafer data 2264. The CLiCS system 2266 uses the stepsshown in FIGS. 46A and 46B and FIGS. 47A and 47B to perform three basicfunctions described in 2268, 2270 and 2272. The first function is toverify whether a given layout passes or fails the lithography processstep for a given layout design level 2268. The second function is toidentify areas of the layout that exceed design tolerances 2270,(similar to the situation depicted in 2271 also shown in FIG. 49). Thethird function is to modify the layout such that the printed (etched)dimensions and features match the desired values or are within thedesign tolerances 2272. The result is a modified layout that meets allthe design and electrical specifications and yields the desired printed(etched) feature dimensions 2274. The layout is then used to generatethe mask set for lithography 2276.

[0256] In some cases, there may be a large performance benefit tosqueezing parameters well within the design tolerances. This may beaccomplished by either reducing the tolerance limits or iteratingbetween the prediction and correction components (as shown in 2024 of46A or 2084 of 47A) until the error is sufficiently reduced. The cost ofcontinual optimization of design and electrical parameters is that thecomputational burden will likely increase significantly. As such, thisdecision is left to the system user.

[0257] h. Implementations and Uses

[0258] The methods described above may be implemented in softwarerunning on a computer or server that communicates with variouscomponents via a network or through other electronic media. The methodscan be used as a Design for Lithography (DfL) system that verifieswhether a particular circuit design will be created or imaged accuratelyon the wafer or corrects the design where features will not beaccurately reproduced.. DfL incorporates lithography-related,within-chip pattern dependencies into decisions regarding the design andprocess development flow.

[0259] This section will describe how the software may be implementedand how it may communicate with other design and manufacturingcomponents. This section will also describe how the software may be usedwith and within lithography tools and electronic design automation (EDA)tools.

[0260] The components that comprise the method are constructed insoftware (e.g. Java, Tcl, Basic, SQL) and modularized such that themethod may or may not use all the components in the generation ofmeasurement plans. For example, the method may only use process modelsto generate film thickness variation, compare this with designspecifications and determine those locations that are most likely toviolate the specification. The following descriptions describe thegeneral computational framework for the method.

[0261]FIG. 51 shows a useful software architecture described in thefollowing paragraphs. The user 2353 communicates with the system througha graphical user interface (GUI) 2354, such as a web browser. The GUI2354 allows the user to choose and upload electronic layout design filesinto the system and view areas that require modification or areas of thedesign that have been modified by the design for lithography system.When the system is housed within an EDA tool the user may be a designer,and the GUI may be part of the EDA tool.

[0262] In general the GUI, as defined and used throughout this section,allows the user to choose, upload or transfer from another form ofelectronic media, electronic layouts, desired design rules, electricalperformance, or CD variation for the particular device described by thedesign files. The user may also use the interface to select process andelectrical models from a server or transfer or load models from anotherelectronic media source or computer. The user may also use the interfaceto review the results of lithography prediction, design faults andmodifications to the design. These results may be in the form of, forexample:

[0263] histograms and other statistical plots,

[0264] full-chip images of wafer-state (including feature variation) orelectrical parameters at some point in time,

[0265] movies of full-chip topography such as film thickness, dishing,erosion progression during a process step or flow,

[0266] movies of full-chip electrical parameter variation such as sheetresistance, drive current, timing closure issues and capacitance, and

[0267] tables of values.

[0268] The GUI 2354 communicates with a series of software components,services or functions 2355 (referred to here as the service module) thatmanage the flow of information throughout the system to the database andfile system 2358 and computational core processes 2356 as well. Theservices 2355 are modular and serve to initiate the computational coreprocesses 2356 that execute portions of the method and to assemble andformat the content for display in the GUI. The modules may be created asscripts (e.g. in Perl, Java, or Tcl) that enable easier interaction withthe database using embedded SQL code and with the GUI using HTML, XML ordynamic HTML interpretation. These components also allow the ability toinitiate mathematical processes that perform the computation necessaryto determine the correct placement of dummy fill within the layout.

[0269] The service module 2355 communicates with the computational coreof processes and functions 2356 that execute computational steps ofchip-level wafer topography, verification and design correction. Thiscore also does the effective pattern density computation and layoutextractions. This communication may include instructions, data, modelparameters, prediction results in tabular, image or movie forms andpointers to files in the file system.

[0270] The service module 2355 also communicates with electronic ICdesign (EDA) software or layout manipulation software 2357 to manipulatelayout information during extraction or to modify the design layout toyield desired feature dimensions.

[0271] The database 2358 communicates with the service module 2355 viaSQL commands to manage system data such as measurement sites andlocations, user profiles that specify permissions and preferred contentand presentation, user data which may include layout extraction data,design specifications and rules, model parameters for particular toolsand processes, and full-chip prediction results such as surfacetopology, resistance and capacitance. Examples of databases that may beused include Oracle, Informix, Access, SQL Server, and FoxPro. The filesystem 2358 communicates with all the components 280, 300, 750 and 800to retrieve and store information saved as files, typically too large toefficiently store in the database.

[0272] The system may communicate directly with metrology equipment togenerate measurement plans and to receive measurements before and afterlithography processing. The system may also communicate directly withelectronic design (EDA) tools to receive design layouts and to providemodified designs. The system may also communicate directly withelectronic design (EDA) tools and foundries to generate test structuresand test wafers and to develop and supply process flows and recipes tomanufacturing. This communication may be done via a computer network2359 or computer bus.

[0273] If the functionality shown in boxes A 2360 and B 2361 resides onone computer then the system is configured as stand-alone. If A and Breside on different computers and communicate across a network, thesystem is normally considered a client-server configuration. A networkmay include electrical and optical communication via an extranet,intranet, internet or VPN. In some cases both A and B will be part ofthe EDA tool suite and the user, 2353, is a designer.

[0274] Here we describe a few useful operational frameworks for applyingthe system to verify and correct designs to yield desired printed oretched features and dimensions. Other frameworks are also possible.There are three basic computational frameworks described in this sectionthat constitute good methods of operation and delivery of thefunctionality based upon a user's needs. The first framework presentedis a stand-alone configuration, shown in FIG. 52A, where the components280, 300, 750 and 800 of FIG. 10 reside in 2363 and data in and out(2364 and 2365) are accessed from a single computer. The secondframework is a client-server configuration, shown in FIG. 52B, where theGUI resides on a client computer 2367 also shown as box A in FIG. 51,which accesses, via a network 2370 the other components, shown as box Bin FIG. 51, residing on a server or multiple servers, a server farm2371. The communication could be done via internet, intranet or extranetnetworks 2370 and the server may serve one or more clients or users.

[0275] The third framework, FIG. 53, is an extension of theclient-server model that includes communication via a network 2376 withadditional computers that may contain one of more components of thesystem described in sections b. through f. For example, a design housemay utilize the design for lithography tools via the server 2380 butremotely utilize separate computers which house EDA tools 2382 orprocess models or model parameters 2379 and design specifications 2378that are provided by the fab or a process development group. Thisframework also includes the transfer of measurement plan data to controlcomputers on metrology equipment 2381 and the return of actualmeasurements to the server 2380. This framework also includes thetransfer of process related information, such as calibration modelparameters, to and from manufacturing or foundry computer systems 2381to the server 2380. This framework also includes the transfer ofinformation to optical proximity tools 2383 for feature density analysisand design correction.

[0276] The system and methods can be implemented and used as a Designfor Lithography (DfL) system that verifies whether a particular circuitdesign will be created or imaged accurately on the wafer and correctsthe design where features will not be accurately reproduced. The DfLsystem includes components 280, 300, 750 and 800 of FIG. 10 and provideslayout extraction, chip-level topography computation, lithography CDvariation computation, design verification, and design modification. Asshown in FIG. 54, the DfL system 2522 may be used with or implementedwithin electronic design automation (EDA) tools 2500 either directlyintegrated or communicating via bus or network through an applicationprogram interface (API). FIG. 54 illustrates where the DfL system 2522would fit within an EDA tool 2500, for example. Conventional EDA toolsmay have the following components: system-level design 2502, logicsynthesis 2504, design layout 2506, place and route 2508, physicalverification 2510, and signal integrity 2512. Each electronic designfile is used during the tape-out process to create masks 2514 which areused in manufacturing 2516 the production IC. Most design formanufacturing components interact with the physical verification andplace and route components. The DfL system 2522-2525 is not limited towhat component it may interact with and may include place and route2508, physical verification 2510, signal integrity 2512 and eventuallymask creation 2514. However, the most likely role is within the physicalverification component 2510, which ensures that a design abides by therules and constraints provided by manufacturing.

[0277] Potential uses of the DfL system include assistance in theplacement and specification of buffer regions for interconnect vias andlines during place and route. In this use, feature width variation ortopographical variation could aid in determining where electricallyactive features and components should be positioned and how electricalfeatures that allow communication between these components (e.g. viasand lines) may be routed across the device.

[0278] Potential uses of the DfL system include assistance in theplacement and geometrical dimensions of interconnect vias and lines toimprove signal integrity, timing issues and power distribution. In thisuse, feature width variation or topographical variation could aid indetermining what the resulting feature geometries will be afterprocessing and how these electrical features may be modified (e.g.,bloated or shrunk by some percentage to compensate for topographyeffects) geometrically to achieve better circuit performance or betterdevice structural and reliability properties.

[0279] Potential uses of the DfL system include assistance in theplacement and buffer regions for dummy fill added to a design. In thisuse, feature width variation or topographical variation could aid indetermining where dummy or slotting objects should be positioned, thesize of dummy and slotting objects and the buffer distance between dummyand slotting objects and nearby electrically active regions.

[0280] These components may be combined to verify or correct forproblems in the electrical performance. The following example describessuch a method. First, the DfL system could be used to modify features onthe circuit layout. Next, the results would be passed to an RCextraction tool. Then, the RC extraction results would be used tore-simulate the circuit performance. The resulting performance could beverified, or alternatively the circuit performance results could be usedto make further modifications to the design layout. In addition, severaldifferent alternative layout modifications could be made; RC extractionand subsequent simulation run all options, and the best modified layoutchosen based on the circuit simulation performance.

[0281]FIG. 55 illustrates how a design group (or a design house) may usea DfL system 2659 that resides within, is directly bundled with, ordirectly communicates with an EDA tool 2670. Most designs begin withspecifications 2655 that include tolerances on feature size andresolution as well as electrical IC parameters. The design group 2656uses these specifications to guide them during the creation of anintegrated circuit 2657. During the process, one designer or subgroupmay do the logic design 2662. Another designer or subgroup may do thememory design 2664 and yet another may design the analog component 2666.A goal of design for manufacturing is to consider manufacturingconstraints at various stages of design that are generated with an EDAtool 2670. EDA tools may contain several design for manufacturingcomponents and the DfL 2659 system may be one of those components, asshown in FIG. 54. In this use, the DfL system continually verifies andcorrects 2656 designs as the components are designed and added by thedesigners. In this use, DfL system may directly interact with place androute functions, physical verification functions, electrical simulationand extraction functions and optical proximity functions to providefeature width variation data. This process may or may not includeiterative addition of dummy fill (as described in U.S. patentapplication Ser. Nos. 10/165,214, 10/164,844, 10/164,847, and10/164,842, all filed Jun. 7, 2002)as well. In cases, where the systemcannot find any corrections to the layout that achieves the designspecifications, the design group is notified of the design failure 2660.The foundry or manufacturing group provides manufacturing information2672 regarding the calibration of models to specific process tools andrecipes.

[0282] In that the DfL system provides a bridge of information flowbetween the design and manufacturing sides, the DfL system may alsoreside with the manufacturer or on the internet and communicate withdesign tools via a network connection. FIG. 56 illustrates a use of theDfL system 2697 outside of or indirectly communicating with one or moreEDA tools 2680. The design specifications 2682, which include CD orassociated electrical tolerances, are provide to both the design group2684 and the design for manufacturing components 2694. The designers usethe EDA tool suite to create and add components 2686, 2688 and 2690 intothe IC layout 2686.

[0283] Each design level is completed 2692 and electronicallytransferred 2696 via media, network or the internet to the design formanufacturing components 2694, which includes the DfL system 2697. Thisframework includes the use of the DfL component as a web service thatcommunicates via the internet with both the design and manufacturinggroups. Each design level is processed using process information 2693,which includes calibration parameters regarding specific tools andrecipe settings. Corrections to the design are uploaded to the EDA tooland server 2698. In cases where the system cannot find any correctionsto the layout that achieves the design specifications, the design groupis notified of the design failure 2699. In the framework shown in FIG.57 the DfL system may:

[0284] reside within tools in the lithography process flow andcommunicate via a bus or network connection,

[0285] reside within an etch tool and communicate via a bus or networkconnection,

[0286] reside on a network at a foundry that allows for process,lithography (etch) models to be developed and managed by manufacturingor process development personnel,

[0287] reside on a server physically located away from both the designand manufacturing groups and communicates via a network, for example, asa web service, or

[0288] reside at a design house or group but outside of a specific EDAtool and may include network communication with a number of EDA toolsfrom different vendors, or

[0289] reside at a foundry and may communicate via a network with anumber of EDA tools from different vendors.

[0290] As shown in FIGS. 55 and 56, the DfL system may be used within alarger design for manufacturing system or server. An example of a designfor manufacturing system is shown in FIG. 57. An IC design of one ormore levels is loaded 2800 and key pattern dependent parameters may beextracted. Process models or simulations of one or more steps 2802 andthat may be calibrated to tools and recipes 2804 and 2806 are used topredict full-chip topography 2808 (such as film thickness, dishing orerosion) or electrical parameters 2808 (such as sheet resistance,capacitance, cross-talk noise, drive current, timing closure values oreffective dielectric constant). Desired results such as physical andelectrical parameters and critical dimension tolerances, often derivedfrom the design specifications, are loaded into the system 2812. Acomparison is performed 2810 and those sites or IC features that exceedthe specified tolerances and the associated variation 2814 and 2816 areused to make corrections within the design or manufacturing processes.

[0291] The variation may be used as feedback to facilitate changes inthe design process through use of a dummy fill component 2818 where thesize and placement of dummy fill is determined and the design modified2822. The selection and placement of dummy fill within an IC designlevel may include the use of pattern dependencies to improve thephysical and structural makeup (e.g. use of low-k materials) andelectrical performance of the IC. When the variation is primarily due tolithography or the combination of surface variation and lithography, theDfL system or component 2820 may be used to modify 2822 the IC design2800.

[0292] The variation 2814 may be used to modify process parameters andrecipe settings as well 2824. This component uses models calibrated atmultiple recipe settings and using various consumables to determine thebest known process and consumable set. This component may provide thisinformation to a tool operator or modify tool recipe settings directly2826. This component may also be used to synthesize multiple processrecipe steps within a flow such that design parameters are optimized.The process optimization component may be used in conjunction with theDfL component 2820 to evaluate lithography tool settings and consumables(such as photoresist materials) with regard to yield and feature sizevariation. This component may also be used to generate measurementrecipes 2825 for measurements to be taken during calibration or actualmanufacture of the circuit 2825 (Additional information concerningselection of measurement locations is found in U.S. patent applicationSer. No. 10/200,660, filed Jul. 22, 2002.)

[0293] Once the design and manufacturing process parameters aresynchronized to yield an optimal circuit, the electronic design is usedto tape-out and create the masks used for lithography, including theaddition of dummy fill structures within the design. The optimal processand measurement recipes may also be transferred to respective toolswithin the manufacturing flow used to create the production circuit.

[0294] The DfL component may also be used to choose an optimallithography recipe among lithography tool settings and consumables (e.g.photoresist). In this use, multiple recipes for the process stepsleading up to and including lithography are evaluated using test wafersdescribed in section g. and the calibration process described in sectionb. A new IC design can be loaded into the system and the process andlithography models evaluated across the multiple recipe calibrations toarrive at minimal feature size variation from the desiredspecifications. An illustration is shown in FIG. 58 where the systemuses the process described in FIG. 10 to predict first pass feature sizevariation or to iterate until an optimize printed feature size isreached for each set of calibration parameters associated with a recipecondition 2901, 2902, and 2903. The results are compared and the optimalrecipe setting is determined 2904. The calibration parameters for eachrecipe condition may be generated using the processes and test wafersdescribed above. The design for manufacturing system may also employoptimization methods to interpolate or synthesize among lithographyprocess flow recipe conditions.

[0295] Several screenshots of graphical user interfaces (GUIs) fordesign for manufacturing and design for lithography systems are shown inthe following figures. A GUI for the Layout Manager component, shown inFIG. 59, allows the user to upload a layout through a web browser andweb services, which are automatically configured to add dummy fill forthe appropriate processes and according to user defined design rules(also input through a similar GUI). The three designs, 3161, 3162 &3163, were processed using the layout extraction algorithm to computeeffective density. Options are provided to the user to use our layoutextraction methods to compute feature width and space or to upload thisinformation from another source, 3164, 3165 & 3166.

[0296] The results of a layout extraction using the system are shown inthe images in FIGS. 60A and 60B. FIG. 60A shows a full-chip image 3167of extracted feature widths (line widths in this case) across the chipaccording to the scale shown on the right 3168. In FIG. 60B, the spatialline widths across the full-chip are shown 3169, 3170, 3171, 3172, 3173,3174 and 3175 according to which line width bin they fall into anduseful distributions may be formed. This information, as well as linespace, local and effective density may be input into the models topredict process and electrical variation.

[0297] A graphical user interface (GUI) for a design for lithographycomponent is shown in FIG. 61, operating within a design formanufacturability server, GUI shown in FIG. 62. A browser 3300 is usedas the GUI that communicates with a web server based DfL componentresiding on a server. The benefit of using a browser is that almostevery computer is now equipped with a web browser and there is a greatdeal of standardization across the two major browsers from Netscape andMicrosoft. A full-chip topography image 3302 is shown and those sites(e.g., 1, 2 and 3) that violate feature dimension tolerances areindicated 3304. The site locations are also shown 3306. A button isshown that initiates the correction component that modifies the designto pass design tolerances 3308.

[0298] The GUI for the design for manufacturing component is shown inFIG. 62 and a good implementation again uses a web browser as the GUI.The dummy fill services and functions are grouped within the GUI intothree primary components; design (4199), manufacture (4191) and model(4200). The screenshot in FIG. 62 shows in the header, 4190, and in thenavigation bar, 4191, that the user has selected the manufacturingcomponent. Within the manufacture component are subcomponents: fabs,tools, wafers, and measurement data. In this screenshot, tools, 4192,have been selected. There are three subcomponents under tools: types,recipes and flows. In this screenshot the user has selected types 4193.The types of tools and tool settings available to this user are shown4194. The available recipes for this tool type 4196 and available recipesequences 4197 for these tool types are shown. The system configured inthis screenshot has two process models available to the user 4198 forcalibration and prediction of copper CMP. The design component 4199 usesa layout manager to allow the user to upload and manage layouts andlayout extractions. One goal of the design for manufacturability systemGUI is to allow the user to manage all the data and results associatedwith design for lithography services.

[0299] Although some implementations have been described above, otherimplementations are also within the scope of the following claims.

1. A method comprising generating a mask design for patterning a testwafer using a lithographic or etch process, characterizing the processbased on the patterned test wafer, and using a pattern-dependent modelbased on the characterization to predict characteristics of integratedcircuits that are to be fabricated by the lithographic or etch process.,2. The method of claim 1 in which the mask design comprises various lineand space dimensions.
 3. The method of claim 1 in which the mask designincludes multiple masks for multiple levels, each of the masks includingvarious line and space dimensions.
 4. The method of claim 1 in which theprocess comprises a preselected lithography or etch tool or lithographyor etch recipe.
 5. The method of claim 1 in which the process comprisespreselected power settings.
 6. The method of claim 1 in which theprocess comprises preselected etch times.
 7. The method of claim 1 inwhich the process comprises preselected polish times.
 8. The method ofclaim 1 in which the process comprises preselected deposition times. 9.The method of claim 1 in which the process comprises preselectedpressures.
 10. The method of claim 1 also including using an electronicsdesign automation (EDA) tool in conjunction with at least one of thegenerating, the characterizing, and the predicting.
 11. The method ofclaim 1 in which at least one of the generating, the characterizing, andthe predicting is provided as a service in a network.
 12. The method ofclaim 11 in which the network comprises an intranet, an extranet, or aninternet, and the generating, the characterizing, and the predicting isprovided in response to user requests.
 13. The method of claim 1 inwhich the recipe includes a preselected photoresist.